Large language model trained on clinical oncology data predicts cancer progression

Probably not a substantial claim: may not pass S3 (load-bearing).

Woollie excels in eight non-medical benchmarks.

“Woollie surpasses ChatGPT in medical benchmarks and excels in eight non-medical benchmarks.”

May combine two claims.

Woollie is an open-source, oncology-specific LLM trained on real-world data from Memorial Sloan Kettering Cancer Center (MSK) across lung, breast, prostate, pancreatic, and colorectal cancers, with external validation using University of California, San Francisco (UCSF) data.

“We introduce Woollie, an open-source, oncology-specific LLM trained on real-world data from Memorial Sloan Kettering Cancer Center (MSK) across lung, breast, prostate, pancreatic, and colorectal cancers, with external validation using University of California, San Francisco (UCSF) data.”

May combine two claims.

For COQA, Woollie 65B achieved accuracy 0.78 versus Llama 65B 0.74, and F1 0.82 versus 0.77.

“For the COQA benchmark, the Woollie 65B model achieved an accuracy of 0.78 compared to 0.74 for the Llama 65B model, and an F1 score of 0.82 versus 0.77.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Stylistic differences between MSK and UCSF reports exist.

“Although we illustrate stylistic differences between MSK and UCSF reports (Supplementary Note 6), future work should consider incorporating imaging data into a multimodal LLM framework to mitigate such limitations and enhance model robustness.”

May combine two claims.

When evaluated within oncology, the Guanaco 65B model's performance did not measure up to Woollie MSK 7B, and it fell short compared to Woollie 33B without finetuning on the MSK dataset.

“When we evaluated its performance within our specific field of oncology, despite its substantial 65 billion parameters—a figure nine times greater than that of a 7 billion parameter model—its performance did not measure up to our Woollie MSK 7B model. It even fell short compared to the Woollie 33B model without finetuning on the MSK dataset (Supplementary Fig. 6).”

• cites [36] Dettmers, T., Pagnoni, A., Holtzman, A. & Zettlemoyer, L. Qlora: Efficient finetuning of quantized LLMs. In Advances in neural information processing Systems 36 (NIPS, 2024).Not checked yet

  Not checked in the free run.

May combine two claims.

The cross-institution validation of the Woollie model, validated on UCSF data, demonstrates that the smaller 7B model performs comparably and even slightly outperforms its larger 33B counterparts.

“The cross-institution validation of the Woollie model, validated on UCSF data, demonstrates that the smaller 7B model performs comparably and even slightly outperforms its larger 33B counterparts.”

May combine two claims.

The model's generalizability is validated, with performance metrics of AUROC illustrating the model's capability to classify tumor progression accurately.

“The model’s generalizability is validated, with performance metrics of AUROC illustrating the model’s capability to classify tumor progression accurately.”

May combine two claims.

Woollie MSK 7B and 33B achieved AUROCs of 0.89 and 0.88, with accuracies of 0.80 and 0.78, respectively, in detecting cancer progression on the UCSF dataset.

“The Woollie MSK 7B and 33B models were tested on this dataset and achieved AUROCs of 0.89 and 0.88, with accuracies of 0.80 and 0.78, respectively, in detecting cancer progression”

• AssumptionRests on an assumption: Radiology impression notes contain sufficient information to determine cancer progression.
• AssumptionRests on an assumption: The radiologist-assigned progression labels are a valid reference standard for cancer progression.

Classified as probably not a substantial claim. May combine two claims.

Woollie highlights terms like "FDG" and "Increase" in notes labeled as "Progressing," while also recognizing the influence of words such as "small" and "mild" in notes classified as "Not Progressing."

“Woollie highlights terms like “FDG” and “Increase” in notes labeled as “Progressing,” while also recognizing the influence of words such as “small” and “mild” in notes classified as “Not Progressing.””

May combine two claims.

Woollie MSK 33B improved over baseline Woollie on PubMedQA from 0.80 to 0.83, MedMCQA from 0.45 to 0.48, and USMLE from 0.49 to 0.53.

“Compared to the baseline Woollie models, the accuracy of Woollie MSK 33B increased from 0.80 to 0.83 in PubMedQA, from 0.45 to 0.48 in MedMCQA, and from 0.49 to 0.53 in USMLE.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.

Woollie adeptly summarizes the salient themes within medical radiology reports, pinpointing intricate details of disease progression and the exact locations of metastatic spread amidst a wealth of medical data.

“Woollie adeptly summarizes the salient themes within medical radiology reports, pinpointing intricate details of disease progression and the exact locations of metastatic spread amidst a wealth of medical data.”

May combine two claims.

Woollie MSK 7B improved over baseline Woollie on PubMedQA from 0.72 to 0.79, MedMCQA from 0.32 to 0.35, and USMLE from 0.33 to 0.38.

“A similar trend was observed in the smaller Woollie MSK 7B model, where accuracy improved from 0.72 to 0.79 in PubMedQA, from 0.32 to 0.35 in MedMCQA, and from 0.33 to 0.38 in USMLE”

May combine two claims.

Woollie MSK 33B achieved AUROC 0.97, compared to Llama 33B AUROC 0.87.

“Similarly, Woollie MSK 33B surpassed the baseline Llama 33B model with an AUROC of 0.97, compared to 0.87”

• AssumptionRests on an assumption: Radiology impression notes contain sufficient information to determine cancer progression.
• AssumptionRests on an assumption: The radiologist-assigned progression labels are a valid reference standard for cancer progression.

May combine two claims.

Although the 33B model's AUROC was slightly lower than the 7B's, its precision in detecting cancer progression was higher at 0.95 compared to 0.78.

“A precision comparison across models, including the baseline Llama (Fig. 5e), using both MSK and UCSF datasets revealed that although the 33B model’s AUROC was slightly lower than the 7B’s, its precision in detecting cancer progression was higher at 0.95 compared to 0.78”

• AssumptionRests on an assumption: Radiology impression notes contain sufficient information to determine cancer progression.
• AssumptionRests on an assumption: The radiologist-assigned progression labels are a valid reference standard for cancer progression.

May combine two claims.

Woollie MSK 7B outperformed all competing 65B models (Llama, Woollie Foundation, Woollie Medicine, Woollie) in binary classification of cancer progression, achieving overall accuracy 0.86 across all disease sites in the rad_imp test.

“When evaluating cancer progression as a binary classification within the test dataset, Woollie MSK 7B outperformed all competing 65B models (Llama, Woolie Foundation, Woollie Medicine, Woollie), achieving an overall accuracy of 0.86 across all disease sites in the “rad_imp” test.”

• AssumptionRests on an assumption: Radiology impression notes contain sufficient information to determine cancer progression.
• AssumptionRests on an assumption: The radiologist-assigned progression labels are a valid reference standard for cancer progression.

Classified as probably not a substantial claim. May combine two claims.

Woollie distilled the primary themes of each report, exhibiting not just the presence or absence of disease progression but further detailing the sites of disease progression.

“Woollie distilled the primary themes of each report (Fig. 6a–f), exhibiting not just the presence or absence of disease progression but further detailing the sites of disease progression.”

May combine two claims.

Woollie 65B performance on PubMedQA matched GPT-4's accuracy of 0.804 and significantly surpassed ChatGPT (3.5-turbo)'s accuracy of 0.7165.

“Notably, the Woollie 65B model’s performance on PubMedQA matched GPT-4’s accuracy of 0.804 and significantly surpassed ChatGPT (3.5-turbo)’s accuracy of 0.7165”

May combine two claims.

Woollie 65B achieved accuracies of 0.81 on PubMedQA, 0.50 on MedMCQA, and 0.52 on USMLE, exceeding Llama 65B scores of 0.70, 0.37, and 0.42.

“Specifically, the Woollie 65B model marked substantial improvements, achieving accuracies of 0.81 on PubMedQA, 0.50 on MedMCQA, and 0.52 on USMLE—exceeding the Llama 65B model’s scores of 0.70, 0.37, and 0.42, respectively”

May combine two claims.

Llama performance generally improved with size, except on PubMedQA where accuracy was 0.71 for 7B, 0.67 for 13B, 0.66 for 33B, and 0.70 for 65B.

“Llama showed that performance generally improved with size, except on the PubMedQA tests, where the accuracy was 0.71 for 7B, 0.67 for 13B, 0.66 for 33B, and 0.70 for 65B”

Probably not a substantial claim: may not pass S2 (checkable).

By preserving general domain competencies of reasoning, conversation, and information extraction while enhancing medical domain proficiency, the aligned models demonstrated a strong foundation for further specialization.

“By preserving general domain competencies of reasoning, conversation, and information extraction while enhancing medical domain proficiency, the aligned models demonstrated a strong foundation for further specialization.”

May combine two claims.

On PubMedQA, Woollie 33B achieved accuracy 0.7980, while 65B reached 0.8092.

“For instance, on the PubMedQA benchmark, the Woollie 33B model achieved an accuracy of 0.7980, while the 65B model reached 0.8092.”

May combine two claims.

As more medical datasets were integrated, non-medical performance slightly declined while medical domain proficiency increased.

“As more medical datasets were integrated into the alignment process, a distinct trend appeared: non-medical performance slightly declined while medical domain proficiency increased.”

May combine two claims.

Woollie All outperformed baseline Llama models but had significant performance gaps compared to Woollie models aligned with the stacked alignment strategy.

“While Woollie All outperformed the baseline Llama models, significant performance gaps were evident when compared to Woollie models aligned with the stacked alignment strategy.”

May combine two claims.

Woollie MSK 7B achieved AUROC 0.94 across all disease sites, outperforming base Llama 7B AUROC 0.50.

“Comparing the Woollie MSK models to the Llama models, Woollie MSK 7B achieved an impressive AUROC of 0.94 across all disease sites, significantly outperforming the base Llama 7B model’s AUROC of 0.50.”

• AssumptionRests on an assumption: Radiology impression notes contain sufficient information to determine cancer progression.
• AssumptionRests on an assumption: The radiologist-assigned progression labels are a valid reference standard for cancer progression.

May combine two claims.

Larger models generally yield better performance in binary classification, but Woollie 65B accuracy slightly lagged behind 33B, with 0.77 versus 0.79.

“The binary classification results indicated that larger models generally yield better performance; however, interestingly, the Woollie 65B model’s accuracy slightly lagged behind the 33B model, with accuracy of 0.77 versus 0.79 for the 33B model.”

May combine two claims.

On PubMedQA, Woollie 33B achieved accuracy 0.79 versus Woollie All 33B 0.70.

“Similarly, on PubMedQA, Woollie 33B achieved an accuracy of 0.79, significantly outperforming Woollie All 33B at 0.70.”

May combine two claims.

Model performance differed significantly by cancer type (Supplementary Table 5b), with all associated p-values below 0.01.

“In contrast, model performance differed significantly by cancer type (Supplementary Table 5b), with all associated p-values below 0.01.”

May combine two claims.

The 7B model is more economically viable for clinical applications due to its lower memory footprint, reduced energy consumption, and less demanding CPU and GPU requirements.

“When comparing the resource usage of deploying 33B models versus smaller 7B models, we found that the 7B model is more economically viable for clinical applications due to its lower memory footprint, reduced energy consumption, and less demanding CPU and GPU requirements.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

The proportion of Not Progressing labels is roughly equal to that of Progressing labels, except in lung cancer cases, which show a slight skew toward Not Progressing.

“The proportion of “Not Progressing” labels is roughly equal to that of “Progressing” labels, except in lung cancer cases, which show a slight skew toward “Not Progressing””

May combine two claims.

Performance improvement was observed from 0-shot to 1-shot learning, but further increases in the number of shots showed minimal improvement or, in some cases, a decline in performance.

“While we observed a performance improvement from 0-shot to 1-shot learning, further increases in the number of shots showed minimal improvement or, in some cases, a decline in performance.”

May combine two claims.

Examination of fourteen distinct Woollie models revealed a slight decrease in general performance following domain-specific training that was not otherwise substantial while demonstrating marked improvement in domain-specific knowledge.

“Our examination of fourteen distinct Woollie models revealed a slight decrease in general performance following domain-specific training that was not otherwise substantial while demonstrating marked improvement in domain-specific knowledge.”

Probably not a substantial claim: may not pass S4 (based). May combine two claims.

Training tailored to one proprietary dataset improved performance in the oncology domain and enhanced performance across other general medical domains and oncology institutions.

“Training tailored to one proprietary dataset improved performance in the oncology domain and enhanced performance across other general medical domains and oncology institutions.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Out of 38,719 impression notes, 2467 contained physician names, which were masked as [[PERSON]].

“Out of 38,719 impression notes, 2467 contained physician names, which we masked as [[PERSON]].”

Probably not a substantial claim: may not pass S4 (based).

High scores on standard medical benchmarks do not necessarily equate to increased effectiveness when using real-world data in healthcare, especially in oncology.

“However, achieving high scores on these benchmarks does not necessarily equate to increased effectiveness when using RWD in healthcare practices, especially in the oncology sector.”

May combine two claims.

Specialized medical models frequently lack in-depth expertise in oncology and have not been validated across multiple institutions.

“Like their general domain counterparts, these specialized medical models frequently lack in-depth expertise in oncology and have not been validated across multiple institutions.”

• cites [13] Bolton, E. et al. Biomedlm: A 2.7 b parameter language model trained on biomedical text. Preprint at https://arxiv.org/abs/2403.18421 (2024).Not checked yet

  Not checked in the free run.

• cites [14] Luo, R. et al. BioGPT: generative pre-trained transformer for biomedical text generation and mining. Brief. Bioinformatics 23, https://doi.org/10.1093/bib/bbac409 (2022). sourceNot checked yet

  Not checked in the free run.

• cites [15] Yang, X. et al. A large language model for electronic health records. npj Digital Med. 5, 194. https://doi.org/10.1038/s41746-022-00742-2 (2022). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

Configuring inference settings with temperature 0.01 and top_p 1.0 effectively minimizes randomness, ensuring deterministic model behavior.

“When utilizing Woollie as a classifier, we configured the inference settings with a temperature of 0.01 and top_p of 1.0. This setup effectively minimizes randomness, ensuring deterministic model behavior.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Reliance solely on free-text radiology impressions may introduce biases tied to institutional reporting styles.

“Additionally, reliance solely on free-text radiology impressions may introduce biases tied to institutional reporting styles.”

Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

Woollie's proficiency indicates potential for comprehending the intent behind medical imaging and for mapping out and succinctly encapsulating the complex pathways of disease evolution across diverse organ systems.

“This proficiency indicates Woollie’s potential not just for comprehending the intent behind medical imaging but also for mapping out and succinctly encapsulating the complex pathways of disease evolution across diverse organ systems (Fig. 6).”

Probably not a substantial claim: may not pass S3 (load-bearing).

The LLM can serve as a foundational model that can adapt to various tasks, particularly in medical contexts.

“This underscores the LLM’s role as a foundational model that can adapt to various tasks, particularly in medical contexts.”

May combine two claims.

Woollie's efficacy in general medical contexts is underscored and the stacked alignment strategy is validated.

“These results underscore Woollie’s efficacy in general medical contexts and validate the effectiveness of our stacked alignment strategy”

Probably not a substantial claim: may not pass S3 (load-bearing).

Our approach, using a single model, contrasts with similar studies on metastatic disease that employ multiple models.

“Our approach, using a single model, contrasts with similar studies on metastatic disease that employ multiple models35, offering potential for new insights into disease progression pathways that have not been thoroughly explored yet.”

• cites [35] Do, R. K. G. et al. Patterns of metastatic disease in patients with cancer derived from natural language processing of structured CT radiology reports over a 10-year period. Radiology 301, 115–122(2021). sourceNot checked yet

  Not checked in the free run.

May combine two claims.

Despite the marginal decrease in non-medical domain performance, the aligned models' capabilities remained robust and competitive.

“Despite the marginal decrease in non-medical domain performance among the aligned models, their capabilities remained robust and competitive.”

Classified as probably not a substantial claim. May combine two claims.

Using LLMs in oncology for purposes like tracking cancer progression and analyzing reports proves viable and advantageous.

“Considering the cost implications, using LLMs in oncology for purposes like tracking cancer progression and analyzing reports proves viable and advantageous.”

May combine two claims.

Woollie models demonstrated effective knowledge transfer, affirming their utility in diverse oncological settings across institutions.

“Although there was a slight drop in performance compared to the MSK data, the Woollie models demonstrated effective knowledge transfer, affirming their utility in diverse oncological settings across institutions.”

• AssumptionRests on an assumption: Radiology impression notes contain sufficient information to determine cancer progression.
• AssumptionRests on an assumption: The radiologist-assigned progression labels are a valid reference standard for cancer progression.

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

These variations likely reflect differences in reporting style and diagnostic emphasis across anatomical sites, which influence the content and structure of radiology impressions.

“These variations likely reflect differences in reporting style and diagnostic emphasis across anatomical sites, which influence the content and structure of radiology impressions.”

May combine two claims.

Models with a more extensive parameter base before alignment yielded better outcomes, with performance scaling almost linearly between 7B and 33B.

“This trend affirmed that models with a more extensive parameter base before alignment yielded better outcomes, with the performance scaling almost linearly with sizes between 7B and 33B.”

Classified as probably not a substantial claim. May combine two claims.

Aligning and fine-tuning LLMs within oncology without losing prior knowledge can be achieved by selecting high-quality datasets and employing a stacked alignment methodology.

“This can be achieved by selecting high-quality datasets and employing a stacked alignment methodology.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.

Woollie's capabilities extend beyond mere data interpretation to an in-depth understanding of cancer biology, positioning it well beyond conventional topic modeling methods.

“Woollie’s capabilities extend beyond mere data interpretation to an in-depth understanding of cancer biology, positioning it well beyond conventional topic modeling methods.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

The 7B model's smaller parameters make it an attractive option for widespread clinical use, capable of running on a single GPU for inference, eliminating the need for specialized centralized computing resources.

“The 7B model’s smaller parameters makes it an attractive option for widespread clinical use, capable of running on a single GPU for inference, thus eliminating the need for specialized centralized computing resources.”

• AssumptionRests on an assumption: Performance on retrospective radiology impression datasets is indicative of potential clinical utility as real-time assistants for oncologists.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

The discrepancy suggests a common trade-off in larger models like the 33B, which typically exhibit low bias but high variance, making them adept at fitting training data closely.

“This discrepancy suggests a common trade-off in larger models like the 33B, which typically exhibit low bias but high variance, making them adept at fitting training data closely.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Underrepresentation in specific subgroups may impact model robustness.

“While radiology impression narratives appear largely independent of these demographic factors, underrepresentation in specific subgroups may impact model robustness.”

• AssumptionRests on an assumption: The datasets from MSK and UCSF differ in the sociodemographic composition of their patient populations.

Probably not a substantial claim: may not pass S2 (checkable).

Choosing a baseline model with adequate parameters is crucial for successful alignment and fine-tuning, as the knowledge acquired during pre-training plays a significant role in performance on downstream tasks.

“Additionally, choosing a baseline model with adequate parameters is crucial for successful alignment and fine-tuning, as the knowledge acquired during pre-training plays a significant role in performance on downstream tasks.”

May combine two claims.

The approach fortified the model's foundation and optimized it for oncology-specific applications, demonstrating improved performance upon further fine-tuning with oncology datasets.

“Our approach fortified the model’s foundation and optimized it for oncology-specific applications, demonstrating improved performance upon further fine-tuning with oncology datasets.”

Classified as probably not a substantial claim.

Predictions are based solely on the knowledge acquired during the model's alignment and fine-tuning processes.

“Predictions are based solely on the knowledge acquired during the model’s alignment and fine-tuning processes.”

Classified as probably not a substantial claim. May combine two claims.

Combining insights from LLM-based topic modeling and LIME explanations highlights the knowledge acquired by the Woollie models and bolsters confidence in their explainability for clinical use.

“Combining insights from LLM-based topic modeling and LIME explanations highlights the knowledge acquired by the Woollie models and bolsters confidence in their explainability for clinical use.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

All MSK patients included in this study were part of IRB-approved research protocols, and the study received independent approval from UCSF; patients provided written, informed consent and were enrolled in a continuous, non-random fashion.

“All MSK patients included in this study were part of institutional review board (IRB)-approved research protocols (MSK IRB Protocols 12–245). Additionally, the study received independent approval from the UCSF (UCSF IRB Protocols 20–32527). Patients provided written, informed consent and were enrolled in a continuous, non-random fashion.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

This approach helps gauge the extent of cancer biology knowledge the model has assimilated during its fine-tuning phase.

“This approach helps us gauge the extent of cancer biology knowledge the model has assimilated during its fine-tuning phase.”

Probably not a substantial claim: may not pass S3 (load-bearing).

AUROC was selected as a primary performance metric due to its robustness and widespread use in evaluating a model's ability to distinguish between classes across various decision thresholds.

“AUROC was selected as a primary performance metric due to its robustness and widespread use in evaluating a model’s ability to distinguish between classes across various decision thresholds.”

Probably not a substantial claim: may not pass S3 (load-bearing).

The Language Model Evaluation Harness framework is a standard tool used across the open-source LLM community for evaluating model effectiveness.

“To test their performance, we employed the Language Model Evaluation Harness framework50, a standard tool used across the open-source LLM community for evaluating model effectiveness.”

• cites [50] Gao, L. et al. A framework for few-shot language model evaluation (Zenodo, 2021).Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

During evaluations and testing, setting the model's generation temperature to 0.01 and top_p to 1.0 minimized randomness and ensured deterministic behavior; repeated tasks showed no randomness, with performance metrics remaining constant across runs.

“During evaluations and testing, we set the model’s generation temperature to 0.01 and top_p to 1.0 to minimize randomness and ensure deterministic behavior, as the model is being used as a classifier. Additionally, we repeated the same tasks multiple times and observed no randomness, with the performance metrics remaining constants across runs.”

Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

Once fine-tuned, an LLM can serve multiple functions across various subspecialties, making it an invaluable tool for oncology clinics and research.

“Once fine-tuned, an LLM can serve multiple functions across various subspecialties, making it an invaluable tool for oncology clinics and research.”

Classified as probably not a substantial claim. May combine two claims.

HDBSCAN is adept at clustering outputs from LLMs due to its ability to manage clusters with varied densities, autonomously identify the number of clusters, and filter out noise as outlier data.

“HDBSCAN is adept at clustering outputs from LLMs due to its ability to manage clusters with varied densities, autonomously identify the number of clusters, and filter out noise as outlier data.”

• cites [54] Campello, R., Moulavi, D. & Sander, J. (2013). Density-Based Clustering Based on Hierarchical Density Estimates. 7819, 160–172. https://doi.org/10.1007/978-3-642-37456-2_14. sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

This decline may be attributed to the model nearing its maximum context size, causing it to lose focus on content in the middle of the sequence.

“This decline may be attributed to the model nearing its maximum context size, causing it to lose focus on content in the middle of the sequence47.”

• cites [47] Liu, N. F. et al. Lost in the middle: how language models use long contexts. Trans. Assoc. Computational Linguist. 12, 157–173 (2024). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Once downloaded to a local environment, the model can function without an external network connection, allowing it to be used on PHI data while ensuring the dataset remains secure and does not transmit outside the system.

“Once downloaded to a local environment, the model can function without an external network connection, allowing it to be used on PHI data while ensuring the dataset remains secure and does not transmit outside the system.”

Classified as probably not a substantial claim.

The radiologist has manually annotated the dataset.

“The radiologist has manually annotated the dataset.”

Classified as probably not a substantial claim.

AUROC is effective in handling imbalanced datasets by focusing on true positive and false positive rates rather than absolute counts.

“Furthermore, AUROC is effective in handling imbalanced datasets by focusing on true positive and false positive rates rather than absolute counts, providing a balanced evaluation even when class distributions are uneven.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

This capability is essential for oncological tasks where clinicians or researchers require precise answers to questions about tumor progression, diagnostic accuracy, or treatment efficacy based on imaging data.

“This capability is essential for oncological tasks where clinicians or researchers require precise answers to questions about tumor progression, diagnostic accuracy, or treatment efficacy based on imaging data.”

• AssumptionRests on an assumption: OpenAssistant is a Q&A dataset that trains the model to respond effectively to specific and often domain-specific queries.

Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

These skills translate directly into the ability to parse nuanced medical terminology, infer progression or stability.

“These skills translate directly into the ability to parse nuanced medical terminology, infer progression or stability.”

• AssumptionRests on an assumption: OpenAssistant is a Q&A dataset that trains the model to respond effectively to specific and often domain-specific queries.