Can ChatGPT assist authors with abstract writing in medical journals? Evaluating the quality of scientific abstracts generated by ChatGPT and original abstracts
Open accessAbstract
Introduction: ChatGPT, a sophisticated large language model (LLM), has garnered widespread attention for its ability to mimic human-like communication. As recent studies indicate a potential supportive role of ChatGPT in academic writing, we assessed the LLM’s capacity to generate accurate and comprehensive scientific abstracts from published Randomised Controlled Trial (RCT) data, focusing on the adherence to the Consolidated Standards of Reporting Trials for Abstracts (CONSORT-A) statement, in comparison to the original authors’ abstracts. Methodology: RCTs, identified in a PubMed/MEDLINE search post-September 2021 across various medical disciplines, were subjected to abstract generation via ChatGPT versions 3.5 and 4, following the guidelines of the respective journals. The overall quality score (OQS) of each abstract was determined by the total number of adequately reported components from the 18-item CONSORT-A checklist. Additional outcome measures included percent adherence to each CONOSORT-A item, readability, hallucination rate, and regression analysis of reporting quality determinants. Results: Original abstracts achieved a mean OQS of 11.89 (95% CI: 11.23–12.54), outperforming GPT 3.5 (7.89; 95% CI: 7.32–8.46) and GPT 4 (5.18; 95% CI: 4.64–5.71). Compared to GPT 3.5 and 4 outputs, original abstracts were more adherent with 10 and 14 CONSORT-A items, respectively. In blind assessments, GPT 3.5-generated abstracts were deemed most readable in 62.22% of cases which was significantly greater than the original (31.11%; P = 0.003) and GPT 4-generated (6.67%; P<0.001) abstracts. Moreover, ChatGPT 3.5 exhibited a hallucination rate of 0.03 items per abstract compared to 1.13 by GPT 4. No determinants for improved reporting quality were identified for GPT-generated abstracts. Conclusions: While ChatGPT could generate more readable abstracts, their overall quality was inferior to the original abstracts. Yet, its proficiency to concisely relay key information with minimal error holds promise for medical research and warrants further investigations to fully ascertain the LLM’s applicability in this domain.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Prior studies have mostly discussed potential applications and ethical implications of LLMs for scientific writing in medicine overall and, when performance was measured, analyzed often only one (typically proprietary) LLM.
“Prior studies have mostly discussed potential applications and ethical implications of LLMs for scientific writing in medicine overall and – when performance was measured – analyzed often only one (typically proprietary) LLM.12–17”
From Open-source versus proprietary large language models for academic writing in medicine: A controlled evaluation of DeepSeek-R1 and ChatGPT-o1 in neuroradiology (2026) · cites this paper as [16]
The Introduction (page not available) discusses ChatGPT’s potential for scientific writing, and the Discussion addresses risks including bias, fabricated content and plagiarism. The paper also measures abstract-writing performance, but its Discussion explicitly says it evaluated both GPT 3.5 and GPT 4; it therefore does not support the suggestion that this study analyzed only one model. It does not establish what most prior studies did. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00819-6 · Cited by 1,099 · see citing works
Hopewell, S., Clarke, M., Moher, D., Wager, E., Middleton, P., Altman, D. G., Schulz, K. F., & and the CONSORT Group (2008) · PLoS Medicine · doi:10.1371/journal.pmed.0050020 · Cited by 674 · see citing works
How Is ChatGPT’s Behavior Changing Over Time?
Chen, L., Zaharia, M., & Zou, J. (2024) · Harvard Data Science Review · doi:10.1162/99608f92.5317da47 · Cited by 507 · see citing works
How is ChatGPT's behavior changing over time?
Chen, L., Zaharia, M., & Zou, J. (2023) · arXiv · doi:10.48550/arxiv.2307.09009 · Cited by 507 · see citing works
Athaluri, S. A., Manthena, S. V., Kesapragada, V. S. R. K. M., Yarlagadda, V., Dave, T., & Duddumpudi, R. T. S. (2023) · Cureus · doi:10.7759/cureus.37432 · Cited by 403 · see citing works
Analysis of 6.4 million SARS-CoV-2 genomes identifies mutations associated with fitness
Obermeyer, F. H., Jankowiak, M., Barkas, N., Schaffner, S. F., Pyle, J. D., Yurkovetskiy, L., Bosso, M., Park, D. J., Babadi, M., MacInnis, B. L., et al. (2022) · Science · doi:10.1126/science.abm1208 · Cited by 332 · see citing works
Májovský, M., Černý, M., Kasal, M., Komarc, M., & Netuka, D. (2023) · Journal of Medical Internet Research · doi:10.2196/46924 · Cited by 301 · see citing works
ChatGPT: Is this version good for healthcare and research?
Vaishya, R., Misra, A., & Vaish, A. (2023) · Diabetes & Metabolic Syndrome: Clinical Research & Reviews · doi:10.1016/j.dsx.2023.102744 · Cited by 293 · see citing works
Ethics of large language models in medicine and medical research
Li, H., Moon, J. T., Purkayastha, S., Celi, L. A., Trivedi, H., & Gichoya, J. W. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00083-3 · Cited by 286 · see citing works
Expanding Cosmetic Plastic Surgery Research With ChatGPT
Gupta, R., Park, J. B., Bisht, C., Herzog, I., Weisberger, J., Chao, J., Chaiyasate, K., & Lee, E. S. (2023) · Aesthetic Surgery Journal · doi:10.1093/asj/sjad069 · Cited by 112 · see citing works
Generative artificial intelligence: Can <scp>ChatGPT</scp> write a quality abstract?
Babl, F. E., & Babl, M. P. (2023) · Emergency Medicine Australasia · doi:10.1111/1742-6723.14233 · Cited by 73 · see citing works
Hays, M., Andrews, M. A., Wilson, R. L., Callender, D. M., O’Malley, P. G., & Douglas, K. (2016) · BMJ Open · doi:10.1136/bmjopen-2016-011082 · Cited by 66 · see citing works
Eppler, M., Ganjavi, C., Knudsen, J., Davis, R. J., Ayo‐Ajibola, O., Desai, A., Ramacciotti, L. S., Chen, A. T.-Y., Abreu, A. D. C., Desai, M. M., et al. (2023) · Urology Practice · doi:10.1097/upj.0000000000000428 · Cited by 66 · see citing works
Song, S. Y., Kim, B., Kim, I., Kim, S., Kwon, M., Han, C., & Kim, E. Y. (2017) · PLoS ONE · doi:10.1371/journal.pone.0187807 · Cited by 37 · see citing works
Buholayka, M., Zouabi, R., & Tadinada, A. (2023) · Cureus · doi:10.7759/cureus.39386 · Cited by 30 · see citing works
Reporting quality of trial abstracts—improved yet suboptimal: A systematic review and meta‐analysis
Chhapola, V., Tiwari, S., Brar, R., & Kanwal, S. K. (2018) · Journal of Evidence-Based Medicine · doi:10.1111/jebm.12294 · Cited by 26 · see citing works
Speich, B., Cord, K. A. M., Agarwal, A., Gloy, V., Gryaznov, D., Moffa, G., Hopewell, S., & Briel, M. (2019) · World Journal of Surgery · doi:10.1007/s00268-019-05064-1 · Cited by 23 · see citing works
Hua, F., Sun, Q., Zhao, T., Chen, X., & He, H. (2019) · BMJ Open · doi:10.1136/bmjopen-2019-029270 · Cited by 20 · see citing works
Valentín-Bravo, F. J., Mateos-Álvarez, E., Usategui‐Martín, R., Andrés‐Iglesias, C., Pastor-Jimeno, J. C., & Pastor-Idoate, S. (2023) · Archivos de la Sociedad Española de Oftalmología (English Edition) · doi:10.1016/j.oftale.2023.04.011 · Cited by 16 · see citing works
Cindro, P. V., Bukić, J., Pranić, S. M., Leskur, D., Rušić, D., Perišin, A. Š., Božić, J., Vuković, J., & Modun, D. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-054978 · Cited by 13 · see citing works
Asensio-Sánchez, V. M. (2023) · Archivos de la Sociedad Española de Oftalmología (English Edition) · doi:10.1016/j.oftale.2023.06.009 · Cited by 6 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- OpenAI, 2023, GPT-4
Cited by
Sorted by citations
Eger, S., Cao, Y., D’Souza, J., Geiger, A., Greisinger, C., Gross, S., Hou, Y., Krenn, B., Lauscher, A., Li, Y., et al. (2026) · ACM Computing Surveys · doi:10.1145/3845596 · Cited by 122 · see citing works
Artificial intelligence tools expand scientists' impact but contract science's focus
Hao, Q., Xu, F., Li, Y., & Evans, J. (2026) · Nature · doi:10.1038/s41586-025-09922-y · Cited by 117 · see citing works
Ahn, S. (2024) · The Korean Journal of Physiology & Pharmacology · doi:10.4196/kjpp.2024.28.5.393 · Cited by 51 · see citing works
Impact of Large Language Models on Medical Education and Teaching Adaptations
Zhui, L., Yhap, N., Liping, L., Wang, Z., Zhonghao, X., Xiaoshu, Y., Hong, C., Liu, X., & Wei, R. (2024) · JMIR Medical Informatics · doi:10.2196/55933 · Cited by 51 · see citing works
He, W., Zhang, W., Jin, Y., Zhou, Q., Zhang, H., & Xia, Q. (2024) · Journal of Medical Internet Research · doi:10.2196/54706 · Cited by 48 · see citing works
Kim, S.-J. (2024) · Science Editing · doi:10.6087/kcse.343 · Cited by 42 · see citing works
AI in Dental Radiology—Improving the Efficiency of Reporting With ChatGPT: Comparative Study
Stephan, D., Bertsch, A., Burwinkel, M., Vinayahalingam, S., Al‐Nawas, B., Kämmerer, P. W., & Thiem, D. G. E. (2024) · Journal of Medical Internet Research · doi:10.2196/60684 · Cited by 39 · see citing works
Stephan, D., Bertsch, A., Schumacher, S., Puladi, B., Burwinkel, M., Al‐Nawas, B., Kämmerer, P. W., & Thiem, D. G. E. (2025) · Journal of Medical Internet Research · doi:10.2196/73337 · Cited by 35 · see citing works
Estimating the use of ChatGPT in dental research publications
Uribe, S. E., & Maldupa, I. (2024) · Journal of Dentistry · doi:10.1016/j.jdent.2024.105275 · Cited by 27 · see citing works
Generative Artificial Intelligence, AI for Scientific Writing: A Literature ReviewPreprint
Shopovski, J. (2024) · Preprints.org · doi:10.20944/preprints202406.0011.v1 · Cited by 27 · see citing works
Bao, T., Zhao, Y., Mao, J., & Zhang, C. (2025) · Scientometrics · doi:10.1007/s11192-025-05341-y · Cited by 25 · see citing works
How is ChatGPT acknowledged in academic publications?
Kousha, K. (2024) · Scientometrics · doi:10.1007/s11192-024-05193-y · Cited by 23 · see citing works
Spennemann, D. H. R. (2025) · Publications · doi:10.3390/publications13010012 · Cited by 23 · see citing works
Ng, J. Y. (2025) · Integrative Medicine Research · doi:10.1016/j.imr.2025.101222 · Cited by 19 · see citing works
Integrating Artificial Intelligence into Causal Research in Epidemiology
Matthay, E. C., Neill, D. B., Titus, A. R., Desai, S. M., Troxel, A. B., Cerdá, M., Diaz, I. M., Santacatterina, M., & Thorpe, L. E. (2025) · Current Epidemiology Reports · doi:10.1007/s40471-025-00359-5 · Cited by 18 · see citing works
Zhang, M., & Zhang, J. (2025) · Journal of English for Academic Purposes · doi:10.1016/j.jeap.2025.101540 · Cited by 17 · see citing works
Kousha, K., & Thelwall, M. (2026) · Scientometrics · doi:10.1007/s11192-026-05601-5 · Cited by 10 · see citing works
Faiyazuddin, M., Gholap, A. D., Maqsood, S., Abbas, S., Chaudhari, Y. S., Sharmila, S., & Webster, T. J. (2024) · Journal of Biomedical Nanotechnology · doi:10.1166/jbn.2024.3925 · Cited by 10 · see citing works
Is your curriculum GenAI-proof? A method for GenAI impact assessment and a case study
Jongkind, R., Elings, E., Joukes, E., Broens, T., Leopold, H., Wiesman, F., & Meinema, J. G. (2025) · MedEdPublish · doi:10.12688/mep.20815.3 · Cited by 9 · see citing works
Lupton, D. A., & Butler, E. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4871308 · Cited by 8 · see citing works
Zhang, M., & Zhang, J. (2026) · Applied Linguistics · doi:10.1093/applin/amaf032 · Cited by 7 · see citing works
O’Brien, K. A., & Prentice, S. (2025) · Frontiers in Sports and Active Living · doi:10.3389/fspor.2025.1627685 · Cited by 7 · see citing works
Promotion of medical research: Stance in AI-generated and scholar-written highlights
Zou, H., & Fan, Y. (2025) · Journal of English for Academic Purposes · doi:10.1016/j.jeap.2025.101593 · Cited by 6 · see citing works
Dorigo, W., Giacomelli, E., Calvagna, C., Griselli, F., Speziali, S., Fargion, A. T., Lepidi, S., Pulli, R., & D’Oria, M. (2024) · JVS-Vascular Insights · doi:10.1016/j.jvsvi.2024.100107 · Cited by 6 · see citing works
Ng, J. Y. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1745928 · Cited by 5 · see citing works
Alencar‐Palha, C., Ocampo, T. S. C., Silva, T. P., Neves, F. S., & Oliveira, M. L. (2024) · European Journal Of Dental Education · doi:10.1111/eje.13057 · Cited by 5 · see citing works
IAN, an intelligent system for omics data analysis and discovery
Nagarajan, V., Horai, R., Shi, G., Yu, C., Gopalakrishnan, J., Yadav, M. K., Liew, M. H., Gentilucci, C., & Caspi, R. R. (2026) · Cell Reports Methods · doi:10.1016/j.crmeth.2026.101503 · Cited by 3 · see citing works
Ágústsdóttir, D. H., Rosenberg, J., & Baker, J. J. (2025) · Cochrane Evidence Synthesis and Methods · doi:10.1002/cesm.70037 · Cited by 3 · see citing works
Clarity Without Credibility? Human Versus AI Abstracts in Otolaryngology
Hack, S., Attal, R., Levi, L., Liu, D. T., Manavi, M. S., Locatello, L. G., Fieux, M., de Joya, E. M., Gutovitz, J. M., Eliahu, S., et al. (2026) · World Journal of Otorhinolaryngology - Head and Neck Surgery · doi:10.1002/wjo2.70104 · Cited by 3 · see citing works
AI-driven abstract generating: evaluating LLMs with a tailored prompt under the PRISMA-A framework
Demi̇r, G. B., Gökmen, Ş., Süküt, Y., Topsakal, K. G., & Görgülü, S. (2025) · BMC Oral Health · doi:10.1186/s12903-025-06982-4 · Cited by 2 · see citing works
Velasco, A. M., Coelho, E. W. F., Paes, V. L. S., Castro, R. C., & de Oliveira, R. D. C. S. (2025) · Caderno Pedagógico · doi:10.54033/cadpedv22n5-216 · Cited by 2 · see citing works
Kim, H. J., Yoon, J., Goh, T. S., Moon, J. K., Kim, H., & Lee, S.-H. (2026) · Postgraduate Medical Journal · doi:10.1093/postmj/qgag018 · Cited by 2 · see citing works
Zastosowanie modelu językowego ChatGPT do automatycznego generowania ustrukturyzowanych abstraktów
Pulikowski, A. (2025) · Zagadnienia Informacji Naukowej - Studia Informacyjne · doi:10.36702/zin2024.02.10e · Cited by 2 · see citing works
Anand, A., Rizvi, S. W. A., Ravindhran, S., Ajith, R., G, G. K. K., & Goyal, D. (2024) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon62057.2024.10838006 · Cited by 2 · see citing works
The Prompt Engineering Report Distilled: Quick Start Guide for Life SciencesPreprint
Romanov, V., & Niederer, S. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2509.11295 · Cited by 2 · see citing works
Hallucinations in large language models within academic contexts: A systematic review.
Lai, K. E., Mustaffa, N., Preece, C. N., & Wong, F. Y. (2026) · Journal of Science and Technology Policy Management · doi:10.1108/jstpm-01-2026-0015 · Cited by 1 · see citing works
The benefits of plain language summaries in public health publishing
Frazer, K., Radley, D., Homer, C. V., Ells, L. J., Saunders, J. B., & Stickley, T. (2025) · Perspectives in Public Health · doi:10.1177/17579139251335966 · Cited by 1 · see citing works
Turós, M., Soltész-Várhelyi, K., & Szűts, Z. (2026) · Education Sciences · doi:10.3390/educsci16060920 · Cited by 1 · see citing works
Qiu, W., & Pan, F. (2026) · Scientometrics · doi:10.1007/s11192-026-05701-2 · Cited by 1 · see citing works
Eminovic, S., Levita, B., Schmidt, R., Lindholz, M., Desser, D., Penzkofer, T., Wattjes, M. P., & Nawabi, J. (2026) · DIGITAL HEALTH · doi:10.1177/20552076261464745
The Infiltration of Artificial Intelligence Into Higher Education
Jnah, M., & Jnah, A. J. (2024) · Neonatal Network The Journal of Neonatal Nursing · doi:10.1891/nn-2024-0006
Chatgpt-Generated Modifications on Human Generated Tesol Abstracts by Vietnamese ResearchersPreprint
Nguyen, V. N., Hoa, T. M., & Pramoolsook, I. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5117315
Chatgpt-Generated Modifications on Human Generated Tesol Abstracts by Vietnamese ResearchersPreprint
Nguyen, V. N., Pramoolsook, I., & Hoa, T. M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5245273
ChatGpt-generated modifications on human generated TESOL abstracts by Vietnamese researchers
Nguyen, N. V., Hoa, T. M., & Pramoolsook, I. (2025) · Ampersand · doi:10.1016/j.amper.2025.100239
Mau, B.-R., & Feng, H.-H. (2025) · Assessing Writing · doi:10.1016/j.asw.2025.100984
Geoscience Communication: Perceptions and Use of Generative AI in a STEM Discipline
Pickens, F. (2025) · ACM International Conference on Design of Communication (SIGDOC) · doi:10.1145/3711670.3764655
Chai, Y., Huang, Y., & Li, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5772230
Potencjał i granice dużych modeli językowych w generowaniu strukturyzowanych abstraktów
Kamińska, A. M., Tomaszczyk, J., Włodarczyk, B., Drabek, A., Matysek, A., & Pulikowski, A. (2026) · Zagadnienia Informacji Naukowej - Studia Informacyjne · doi:10.36702/zin2026.01.01
Is your curriculum GenAI-proof? A method for GenAI impact assessment and a case study
Jongkind, R., Elings, E., Joukes, E., Broens, T., Leopold, H., Wiesman, F., & Meinema, J. G. (2026) · MedEdPublish · doi:10.12688/mep.20815.2
Can AI mirror the human voice? Stance in scholar-written and ChatGPT-generated medical abstracts
Zou, H., Sun, J., & Li, L. (2026) · English for Specific Purposes · doi:10.1016/j.esp.2026.04.002
Artificial Intelligence for Discovery in Life Sciences
Chanda, S., Rizzoli, S. O., & Shaib, A. (2026) · Bioconjugate Chemistry · doi:10.1021/acs.bioconjchem.6c00230
Olulana, K., Owolawi, P. A., Du, C., & Abe, B. T. (2026) · Journal of Intelligent Systems and Applied Data Science · doi:10.64680/jisads.v4i1.65
A Structural Shift in the Readability of Ophthalmology Abstracts in Late 2023
Neubauer, J., Bartz‐Schmidt, K. U., & Gelişken, F. (2026) · Ophthalmology Science · doi:10.1016/j.xops.2026.101337
Moldur, M. (2024) · Bilkent University Institutional Repository (Bilkent University)
Professional roles, gender, and attitudes toward generative AI: evidence from Japan
Yamamura, E., & Ohtake, F. (2026) · AI and Ethics · doi:10.1007/s43681-026-01299-6
Stephan, D., Schumacher, S., Al‐Nawas, B., Kämmerer, P. W., & Thiem, D. G. E. (2026) · Oral and Maxillofacial Surgery · doi:10.1007/s10006-026-01643-4
Wang, H., & Saeed, M. A. (2026) · International Journal on Studies in Education · doi:10.46328/ijonse.9125
Liu, X., Luo, J., Chen, Y., Zhu, J., Liu, Y., & Wang, Z. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1948768
Do LLMs “Normalize” Abstracts? Evidence From Paired Sequence and Position Analyses of Santos Moves
Qiao, F., Wu, Q., & Zhou, Y. (2026) · SAGE Open · doi:10.1177/21582440261499856
- Authors
- Hwang, T., Aggarwal, N., Khan, P. Z., Roberts, T., Mahmood, A., Griffiths, M. M., Parsons, N., & Khan, S.
- Affiliations
- University Hospitals Coventry and Warwickshire NHS Trust (GB)
- University of Warwick (GB)
- Royal Devon and Exeter Hospital (GB)
- Corresponding author
- Hwang, Taesoon
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2024-02-14
- Volume
- 19
- Issue
- 2
- Pages
- e0297701
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC10866463
- OpenAlex
- W4391811744
- Cited by
- 59
- References
- 20
- Citations by year
- 2026: 22 · 2025: 22 · 2024: 15
- FWCI
- 4.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (publishedVersion)
- Warwick Research Archive Portal (University of Warwick) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Royal Devon and Exeter Research Repository (Royal Devon and Exeter) (submittedVersion, cc-by)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Library and Information Sciences › Academic Writing and Publishing
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- ChatGPT, large language models, scientific abstracts, abstract writing, reporting quality, randomized controlled trials, hallucinations, readability, medical journal publishing, reporting guidelines adherence, publication ethics, plagiarism detection
- MeSH terms
- Hallucinations, Humans, Periodicals as Topic, Publications, Reference Standards, Writing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
