Cited by 390Meta-analysis and systematic reviewsStatistical Methods in EpidemiologyStatistical Methods in Clinical Trials

The DOI matches its Crossref record; 23 of the 25 references it cites were found in a registry or an index.

Understanding and interpreting confidence and credible intervals around effect estimates

Open access

Hespanhol, Luiz, Vallio, Caio Sain, Costa, Lucíola Menezes, Saragiotto, Bruno T·2019·Brazilian Journal of Physical Therapy

DOI 10.1016/j.bjpt.2018.12.006PMC PMC6630113

Open this paper in CoChat — free account Open PDF

Abstract

Introduction Reporting confidence intervals in scientific articles is important and relevant for evidence-based practice. Clinicians should understand confidence intervals in order to determine if they can realistically expect results similar to those presented in research studies when they implement the scientific evidence in clinical practice. The aims of this masterclass are: (1) to discuss confidence intervals around effect estimates; (2) to understand confidence intervals estimation (frequentist and Bayesian approaches); and (3) to interpret such uncertainty measures.Content Confidence intervals are measures of uncertainty around effect estimates. Interpretation of the frequentist 95% confidence interval: we can be 95% confident that the true (unknown) estimate would lie within the lower and upper limits of the interval, based on hypothesized repeats of the experiment. Many researchers and health professionals oversimplify the interpretation of the frequentist 95% confidence interval by dichotomizing it in statistically significant or non-statistically significant, hampering a proper discussion on the values, the width (precision) and the practical implications of such interval. Interpretation of the Bayesian 95% confidence interval (which is known as credible interval): there is a 95% probability that the true (unknown) estimate would lie within the interval, given the evidence provided by the observed data.Conclusions The use and reporting of confidence intervals should be encouraged in all scientific articles. Clinicians should consider using the interpretation, relevance and applicability of confidence intervals in real-world decision-making. Training and education may enhance knowledge and skills related to estimating, understanding and interpreting uncertainty measures, reducing the barriers for their use under either frequentist or Bayesian approaches.

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

Crossref · 10.1016/j.bjpt.2018.12.006

Cites

23 of 23 references verified0 citation errors · 0 not checkable

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Association between Water Intake, Chronic Kidney Disease, and Cardiovascular Disease: A Cross-Sectional Analysis of NHANES Data
Sontrop, J. M., Dixon, S. N., Garg, A. X., Jiménez, I. B., Dohein, O., Huang, S. S., & Clark, W. F. (2013) · American Journal of Nephrology · doi:10.1159/000350377 · Cited by 5,903
Probable Inference, the Law of Succession, and Statistical Inference
Wilson, E. B. (1927) · Journal of the American Statistical Association · doi:10.1080/01621459.1927.10502953 · Cited by 4,476
Confidence intervals rather than P values: estimation rather than hypothesis testing.
Gardner, M. J., & Altman, D. G. (1986) · BMJ · doi:10.1136/bmj.292.6522.746 · Cited by 2,014
Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide
Twisk, J. W. R. (2003) · Cited by 1,244
The fickle P value generates irreproducible results
Halsey, L. G., Curran‐Everett, D., Vowler, S. L., & Drummond, G. B. (2015) · Nature Methods · doi:10.1038/nmeth.3288 · Cited by 787
Statistics in Medicine: Calculating confidence intervals for relative risks (odds ratios) and standardised ratios and rates
Morris, J., & Gardner, M. J. (1988) · BMJ · doi:10.1136/bmj.296.6632.1313 · Cited by 764
Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide
Twisk, J. W. R. (2013) · doi:10.1017/cbo9781139342834 · Cited by 593
Bayesian Biostatistics
Lesaffre, E. M. E. H., & Lawson, A. B. (2012) · Wiley eBooks · doi:10.1002/9781119942412 · Cited by 304
Statistical Inference by Confidence Intervals: Issues of Interpretation and Utilization
Sim, J., & Reid, N. G. (1999) · Physical Therapy · doi:10.1093/ptj/79.2.186 · Cited by 167
A tutorial introduction to Bayesian inference for stochastic epidemic models using Markov chain Monte Carlo methods
O’Neill, P. D. (2002) · Mathematical Biosciences · doi:10.1016/s0025-5564(02)00109-8 · Cited by 161

Flagged first, then by citations

Show all 25 references

2 of the 25 references couldn’t be matched to a known paper.

Research works printed but not linked: 1

Newcombe, 1998, Interval estimation for the difference between independent proportions: comparison of eleven methods, Stat Med, 17, 873, 10.1002/(SICI)1097-0258(19980430)17:8<873::AID-SIM779>3.0.CO;2-I · DOI · Paper page

Sorted by citations

Items 301–389 of 389

Bayesian Maximal Information Coefficient (BMIC) to Discover Causal Correlations from Large Dataset in Random SystemPreprint
Wang, S., & Surapunt, T. (2021) · Research Square · doi:10.21203/rs.3.rs-343006/v1
Bayesian Maximal Information Coefficient (BMIC) to Reason Novel Trends in Large DatasetsPreprint
Wang, S., & Surapunt, T. (2021) · Research Square · doi:10.21203/rs.3.rs-343006/v2
Statistical modelling of compromised longitudinal neuroimaging datasets: an application to alzheimer's disease
Pérez‐Millan, A., Contador, J., Niñerola‐Baizán, A., Tudela, R., Setoaín, X., Lladó, A., Sánchez‐Valle, R., & Sala‐Llonch, R. (2021) · Emerging Topics in Artificial Intelligence (ETAI) · doi:10.1117/12.2593709
Bayesian Spatiotemporal Modeling and Mapping of Infectious Diseases
Jaya, I. G. N. M. (2022) · University of Groningen research database (University of Groningen / Centre for Information Technology) · doi:10.33612/diss.206632600
Bayesian Maximal Information Coefficient (BMIC) to Reason Novel Trends in Large DatasetsPreprint
Wang, S., & Surapunt, T. (2021) · Research Square · doi:10.21203/rs.3.rs-343006/v3
The Most Difference in Means: A Statistic for the Strength of Null and Near-Zero ResultsPreprint
Corliss, B. A., Brown, T., Zhang, T., Janes, K. A., Shakeri, H., & Bourne, P. E. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4115866
Teste de Inteligência não verbal R-1: o tamanho do efeito para as normas de 2018 // R-1 non-verbal intelligence test: the effect size for the 2018 standards
Dias, A. R. (2022) · Revista de Psicologia · doi:10.36517/10.36517/revpsiufc.13.2.2022.9
Physical activity and health during the SARS-CoV2 pandemic
Wilke, J. (2022) · doi:10.21248/gups.71408
Synchronous use of two markers of research: p-value and confidence interval
Subedi, K., & Subedi, N. (2022) · Journal of Gandaki Medical College-Nepal · doi:10.3126/jgmcn.v15i2.50531
Determinants and Spatial Factors of Anemia in Women of Reproductive Age in the DRC: A Bayesian Multilevel Ordinal Logistic Regression Model ApproachPreprint
Abysina, M. S., Kandala, N., Hamuli, E. K., & Batina, S. (2023) · Research Square · doi:10.21203/rs.3.rs-2487772/v1
Chloroprocaine for Spinal or Epidural Anesthesia
Jafari, Z., & Spry, C. (2023) · Canadian Journal of Health Technologies · doi:10.51731/cjht.2023.651
Seasonal Variation of Water Recharging in the Yangtze River Using Hydrogen and Oxygen Stable IsotopesPreprint
Chen, H., & Huang, C. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4457629
Bayesian Estimation for the Difference between Coefficients of Quartile Variation of Delta-Lognormal Distributions: An Application to Rainfall in Thailand
Yosboonruang, N., & Niwitpong, S. (2023) · Symmetry · doi:10.3390/sym15071383
Performance Prediction via Bayesian Matrix Factorisation for Multilingual Natural Language Processing Tasks
Schram, V., Beck, D., & Cohn, T. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.131
The psychology of Mona Lisa’s expressionPreprint
Soranzo, A. (2023) · Research Square · doi:10.21203/rs.3.rs-3593306/v1
A Factor Analytic Evaluation of the Private Club Members' Benefits Scale
Njeri, M. (2023) · doi:10.12794/metadc2179203
Defensive freezing sharpens threat-reward information processing during approach-avoidance decision makingPreprint
Klaassen, F. H., Bramson, B., Schoffelen, J., de Voogd, L. D., & Karin Roelofs, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.29.610250
A New Centralized Statistical Monitoring Method for Detecting Atypical Distribution of Qualitative Variables in Multicenter Randomized Controlled Trials
Niangoran, S., Barbieri, A., Badjé, A., Journot, V., Kouamé, G. M., Marcy, O., Anglaret, X., & Alioum, A. (2024) · Statistics in Biopharmaceutical Research · doi:10.1080/19466315.2024.2404631
Nutritional guidance needs and influence factors for gastric cancer survivors in primary healthcare setting: a cross-sectional survey
Wang, X., Zeng, H., Li, L., Xu, L., Li, J., Gu, W., Shen, C., Li, X., Shi, W., & Xie, L. (2024) · Supportive Care in Cancer · doi:10.1007/s00520-024-08987-1
Bayesian hierarchical model for heterogeneity in the diagnosis and treatment of hepatocellular carcinoma (HCC) in the African continent
Capanu, M., Li, Y., Chou, J. F., Gönen, M., & Abou‐Alfa, G. K. (2024) · Communications in Statistics Case Studies Data Analysis and Applications · doi:10.1080/23737484.2024.2434824
Predicting oxytocin binding dynamics in receptor genetic variants through computational modeling
Dubey, P., Fang, Y., Tukei, K. L., Kuila, S., Liu, X., Sahota, A., Frolova, A. I., Reinl, E. L., Malik, M., England, S. K., et al. (2025) · npj Women s Health · doi:10.1038/s44294-025-00058-y
The role of sleep difficulties in the relation between pain and smoking cessation processes
Thai, J. M., Charles, J. J., Smit, T., Redmond, B. Y., Giordano, T. P., Ditre, J. W., Garey, L., & Zvolensky, M. J. (2025) · Journal of Behavioral Medicine · doi:10.1007/s10865-025-00580-z · Cited by 1
A Spatial-Correlated Multitask Linear Mixed-Effects Model for Imaging Genetics
Pu, Z., & Ge, S. (2025) · Journal of Computational Biology · doi:10.1089/cmb.2024.0721
Statistical Tools for Designing Tests of Mitigated Memories
Quinn, H., Pineda, A. C., & Swift, G. M. (2025) · IEEE Transactions on Nuclear Science · doi:10.1109/tns.2025.3581738
Differences in Perceived Future Impacts of Climate Change on the Workforce Among Residents of British Columbia
Bratu, A., Sharma, A., Logie, C. H., Martin, G., Closson, K., Gislason, M., Hogg, R. S., Takaro, T. K., & Card, K. G. (2025) · Climate · doi:10.3390/cli13080157
Comparison between GPS “Wimu” and video tracking “Golstats” to quantify match running performance in the Liga MX football
Vasquez-Bonilla, A. A., Rendeiro-Pinho, G., Tojo, Ó., Balsom, P. D., & García, L. E. C. (2025) · International Journal of Performance Analysis in Sport · doi:10.1080/24748668.2025.2549143
Defining a long COVID ‘expotype’ within the P4O2 COVID-19 study
Holtjer, J. C. S., Houweling, L., Bloemsma, L. D., Cornelissen, M. E. B., der Zee, A. H. M., Portengen, L., Kakhaia, S., Vermeulen, R. C. H., & Downward, G. S. (2025) · Environmental Research · doi:10.1016/j.envres.2025.122860
A dynamic Bayesian network approach to modeling engagement and walking behavior: insights from a yearlong micro-randomized trial ( Heartsteps II )
Torre, S. A. D. L., Mistiri, M. E., Tung, K., Hekler, E. B., Klasnja, P., Misha Pavel, C., Rivera, D. E., Spruijt‐Metz, D., & Marlin, B. M. (2025) · Health Psychology and Behavioral Medicine · doi:10.1080/21642850.2025.2552479
Multi-cellular network model predicts alterations in glomerular endothelial structure in diabetic kidney disease
Patidar, K., & Versypt, A. N. F. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013598
Estimation of Building Energy Demand Characteristics using Bayesian Statistics and Energy Signature ModelsPreprint
Smertinas, J., Nielsen, N., Hove, M. Y. C. V., Bacher, P., & Madsen, H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5732727
Couple synchrony in physical activity: effects on individuals with knee osteoarthritis
Cho, Y. W., Huang, Y., Chow, S., & Martire, L. M. (2025) · Annals of Behavioral Medicine · doi:10.1093/abm/kaaf092
Bayesian workflow for bias-adjustment model in meta-analysis
Jung, J., & Aloe, A. M. (2025) · Research Synthesis Methods · doi:10.1017/rsm.2025.10050
Early-life viability selection targets deleterious mutations in exons
Chen, R. S., Soulsbury, C. D., van Oers, K., & Hoffman, J. I. (2025) · Current Biology · doi:10.1016/j.cub.2025.10.043
Quality by design optimisation of isothermal dry particle coating for enhanced buccal permeation of vancomycin
Rajabi, A., Iyire, A., Wyatt, D., Koner, J. S., & Mohammed, A. R. (2025) · Scientific Reports · doi:10.1038/s41598-025-29164-2
Diagnostic Accuracy of Ottawa Ankle Rules in Acute Ankle Injuries in Patients Above Five Years of Age
Rehman, A. U., Khan, F., Farooq, H. M., Rashid, U., Zaman, A. U., & Saddiq, S. (2025) · Pakistan Journal of Health Sciences · doi:10.54393/pjhs.v6i11.3390
Exploring African Community Attitudes Towards Mental Illness in Australia: A Cross-Sectional Study
Endrawes, G., & Ogunsiji, O. (2025) · Healthcare · doi:10.3390/healthcare13233115 · Cited by 1
Additional file 1 of Physical Activity During Lockdowns Associated with the COVID-19 Pandemic: A Systematic Review and Multilevel Meta-analysis of 173 Studies with 320,636 Participants
Wilke, J., Rahlf, A. L., Füzéki, E., Groneberg, D. A., Hespanhol, L., Mai, P., de Oliveira, G. M., Robbin, J., Tan, B., Willwacher, S., et al. (2022) · Figshare · doi:10.6084/m9.figshare.21316220
Self-citation
Additional file 1 of Effective dosage and mode of exercise for enhancing cognitive function in Alzheimer's disease and dementia: a systematic review and Bayesian Model-Based Network Meta-analysis of RCTs
Yuan, Y., Yang, Y., Hu, X., Zhang, L., Xiong, Z., Bai, Y., Zeng, J., & Xu, F. (2024) · Figshare · doi:10.6084/m9.figshare.25953312
Additional file 1 of Reproductive and hormonal risk factors for sarcoidosis: a nested case–control study
Dehara, M., Sachs, M. C., Kullberg, S., Grunewald, J., Blomberg, A., & Arkema, E. V. (2022) · Figshare · doi:10.6084/m9.figshare.19029195
Designing Effective Financial Information Delivery in the Era of Information Overload: Consumer Preferences for Messaging-Based Service AttributesPreprint
Joo, S., Lee, H., Jung, Y., Yeon, J., Shin, J., Lee, D., & Maeng, K. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5928851
QIFE: A Novel Efficient Quantum Feature Extraction for Convolution Deep Learning based ClassificationPreprint
Mahargya, I. L., Shidik, G. F., Affandy, A., Pujiono, P., Rustad, S., & Dipojono, H. K. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6045188
Performance of Bayesian Additive Regression Trees (BART)-Survival implementation in Python: A Comparison with Traditional and R-Based BART Survival Analysis MethodsPreprint
Tiegs, J., Raykin, J., Adjei, S., Rochlin, I., & Bratcher, A. M. (2026) · Research Square · doi:10.21203/rs.3.rs-8605537/v1
Cross-national inequalities and public health policy effects of the global retinoblastoma burden from 1990 to 2021 and projections to 2035
Huang, Z., Zhou, N., Yu, B., Wang, Y., Shao, W., Chen, H., Li, M., Luo, J., & Yuan, N. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1753390
Social determinants of health screening and pediatric surgical outcomes
Chicoine, N. H., Barry, D., Cockrell, H. C., Dick, A. A. S., & Greenberg, S. L. M. (2026) · Journal of Pediatric Surgery · doi:10.1016/j.jpedsurg.2026.162945
Sleep and smartphone use: Within and between-person relationships from an objective longitudinal smartphone and wearable data donation study
Decorte, P., Poels, K., Vuye, C., Lembrechts, J. J., BARROS, A. G. D., Couscheir, K., & de Bruijn, G. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001232
Determinants of adoption of climate-smart agriculture (CSA) practices in mushroom farming in Bangladesh
Haq, M. I., Rahman, M., Datta, T., Rakib, M. H., & Nóbrega, D. B. (2026) · Scientific Reports · doi:10.1038/s41598-026-39761-4
Neutrality Boundary Robustness for Meta-Analysis
Heston, T. F. (2026) · medtigo Journal of Medicine · doi:10.63096/medtigo3062417
Bayesian machine learning enables discovery of risk factors for hepatosplenic multimorbidity related to schistosomiasis
Zhi, Y.-C., Anguajibi, V., Oryema, J. B., Nabatte, B., Opio, C., Kabatereine, N. B., & Chami, G. F. (2026) · Nature Communications · doi:10.1038/s41467-026-69528-4
Neck proprioception and mobility improved after upper neck and spine treatment: a randomized controlled trial
Ambalavanar, U., Hossinzehi, H., & Murphy, B. A. (2026) · Experimental Brain Research · doi:10.1007/s00221-026-07258-1
Current practice and guideline adherence in renal dose adjustment for patients with chronic kidney disease: A systematic review and meta‐analysis
ElGeed, H. A., Tehemar, A., Gasimelseed, M., Gharaibeh, A. M., Hadi, M. A., Yusuff, K. B., Zidan, A., & Awaisu, A. (2026) · British Journal of Clinical Pharmacology · doi:10.1002/bcp.70731
Quantifying Macro and Micro-Level Predictors on Consumption Expenditure Using Bayesian Linear Regression Approach
En, L., Ong, T. S., & Lee, Y. (2025) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc68654.2025.11437659
Examining relationships among regional economic conditions, cognitive control, and intergroup bias in the implicit association test: A regional modeling approach
Chaplin, K., Wilson, L., Laukenmann, R., & Calanchini, J. (2026) · Current Research in Ecological and Social Psychology · doi:10.1016/j.cresp.2026.100271
Joint models for a longitudinal semicontinuous biomarker and a terminal event with application to cancer clinical trials
Rustand, D. (2020) · theses.fr (ABES) · doi:10.70675/7591f96fz712cz4650za7cfzd80c59775b1b
How much potential do on-device systems hold in the large language model service market? Focusing on providing sustainable business models
Lim, S., Jo, H., Lee, D., & Shin, J. (2026) · Telematics and Informatics · doi:10.1016/j.tele.2026.102395
Effects of resisted movement training on change-of-direction speed in recreationally active and trained individuals: A systematic review with network meta-analysis and meta-regression
Zhang, M., Granacher, U., Yan, R., Falch, H. N., Ramírez‐Campillo, R., Xu, K., Zhang, X., Li, J., Xu, S., Luo, Y., et al. (2026) · Journal of sport and health science/Journal of Sport and Health Science · doi:10.1016/j.jshs.2026.101141
Long-term monitoring to quantify the success of river restoration through dam removal in the Paulins Kill NJ, USA
Pearson, C. F., DiBlasio, M., Creveling, E., Keller, D. H., Vile, J. S., Allen, M. T., & Barry, B. S. (2026) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2026.1771839
Altered neural oscillatory dynamics underlie reduced anticipatory schema use during event segmentation in adolescents with High-Functioning Autism Spectrum disorder
Limburg, R., Kopp, M., Zhou, X.-Z., Ghorbani, F., Roessner, V., Hommel, B., Beste, C., & Prochnow, A. (2026) · NeuroImage: Clinical · doi:10.1016/j.nicl.2026.103998
Hierarchical Bayesian analysis of prehospital timelines for elderly adults in rural and urban motor-vehicle crashes
Balyagati, P. F., Ngereza, A. S., Kidando, E., Owusu-Danquah, J., Kitali, A. E., & Kalambay, P. (2026) · Journal of Safety Research · doi:10.1016/j.jsr.2026.04.009
Evidence Synthesis via Indirect Treatment Comparisons in the European Framework of Joint Clinical Assessment
de la Cuadra-Grande, A., Arruñada, M., García‐Solís, A., Montero, A. R., & Casado, M. Á. (2026) · Epidemiologia · doi:10.3390/epidemiologia7030064
Efficient confidence interval computation for physics-aware machine learning of diffusion-sorption models
Scheurer, S., Frenner, R., Brünnette, T., Oladyshkin, S., & Nowak, W. (2026) · Frontiers in Water · doi:10.3389/frwa.2026.1813791
Daily Ovarian Hormone Exposure and Loss of Control Eating in Adolescent Girls: A Stage 2 Registered Report
Mason, T. B., Morales, J. C., Smith, K. E., Dunton, G. F., & Pang, R. D. (2026) · International Journal of Eating Disorders · doi:10.1002/eat.70123
Fine-scale spatial and temporal overlap of clouded leopard (Neofelis nebulosa) and Asiatic golden cat (Catopuma temminckii) in a tropical evergreen forest of southern Thailand
Kaewdee, B., He, X., Duengkae, P., & Sukmasuang, R. (2026) · Ecologica Montenegrina · doi:10.37828/em.2026.98.6
Academic adjustment in children and adolescents with adverse childhood experiences: a multi-level meta-analysis
Çi̇men, F., & Seçer, İ. (2026) · European Journal of Psychology of Education · doi:10.1007/s10212-026-01139-5
A comparison of involuntary holds in an urban police department: exploring subject and situational factors
Morabito, M. S., DeZago, L., Savage, J., & Oblath, R. (2026) · Police Practice and Research · doi:10.1080/15614263.2026.2678273
Open-Set Event Recognition System Using Self-Supervised Contrastive Learning in Distributed Fiber Acoustic Sensing
Shao, J., Ren, H., Wang, P., Lu, J. J., Yu, C., Cao, Q., Gu, G., & Xuan, Q. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3699775
AI agent for carbon emission accounting and uncertainty analysis of Shanghai's electricity consumption
An, S., Gao, Y., (11505488), L. L., Xie, Y., Wang, M., Cao, J., Wei, W., & Wei, C. (2026) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2026.148689
Pseudo-label augmentation with rated-voltage covariate for Bayesian lifetime estimation of high-voltage insulation from scarce and censored ALT data
Kim, S., 김동환, Oh, J., & Yoon, H. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.113005
Understanding food insecurity and its relationship with health, psychosocial, and behavioral variables among the Latine population in the North Carolina High Country
Renwick, K. A., & Bogardus, M. (2026) · Ethnicity and Health · doi:10.1080/13557858.2026.2688761
DERIVATIVES OF 1-(3-AMINOPROPYL)-1H-IMIDAZOLE AS POTENTIAL LOW-TOXICITY LOCAL ANESTHETICS
SERDALIYEVA, D. M., Маратовна, С. Э., Khaiitova, M., KALDYBAYEVA, A., Malmakova, A., Yu, V. K., Трубачев, В. В., Balabekova, M. K., Kairanbayeva, G. K., Батагоева, З. Ж., et al. (2026) · Vestnik · doi:10.53065/kaznmu.2026.77.2.009
Microplastic removal from water using mucilage extracted from Opuntia ficus-indica as a natural bio-coagulant
Warrish, A., & Chauhan, J. S. (2026) · Separation Science and Technology · doi:10.1080/01496395.2026.2693511
The Focus of Occupational Therapy and Occupational Therapy Assistant Services in the Partnering for Change School-Based Model
Renahan, K., Rezze, B. D., Phoenix, M. P., & Campbell, W. N. (2026) · Journal of Occupational Therapy Schools & Early Intervention · doi:10.1080/19411243.2026.2684917
Application of Artificial Intelligence Algorithms in the Comprehensive Care of Patients with Breast Cancer
Bartusik‐Aebisher, D., Czech, S., Szpara, J., Paul, A., Xavierselvan, M., & Aebisher, D. (2026) · Algorithms · doi:10.3390/a19070524
Reasoning under uncertainty in graduate health education: a scaffolded framework using large language models
Ugrinowitsch, C., Lamas, L., & Libardi, C. A. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1857036
Evaluating a large language model for identifying key comorbidities in discharge summaries of patients with obesity: a comparative validation study against ICD codes using MIMIC-IV
Suenghataiphorn, T., Boonpiraks, K., Prasitsumrit, V., Kulthamrongsri, N., & Danpanichkul, P. (2026) · International Journal of Obesity · doi:10.1038/s41366-026-02145-9 · Cited by 1
Complementary and alternative therapies in the management of menopausal symptoms: a systematic review and meta-analysis of randomised controlled trials
Ak, E. Y., & Ogul, Z. (2026) · Archives of Gynecology and Obstetrics · doi:10.1007/s00404-026-08517-w
Prenatal androgens and female-advantaged cognition: A systematic review and Bayesian meta-analysis in congenital adrenal hyperplasia
Polito, N. D., Gambarota, F., Hampson, E., & Herlitz, A. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.106877
Broadband Noise Acoustic Stapedial Reflex Thresholds as a Marker of Cochlear and Neural Integrity in Individuals with Normal Audiograms
Liang, Y., Shahnaz, N., & Ciocca, V. (2026) · Audiology Research · doi:10.3390/audiolres16040105
Quantifying target antigen-dependent CAR T-cell performance against AML
Shah, S., Mueller, J., Vogel, E., Raatz, M., Boettcher, S., Traulsen, A., Manz, M. G., & Altrock, P. M. (2026) · Cancer Cell International · doi:10.1186/s12935-026-04419-8 · Cited by 1
A Guide to Transformations for Standardized Mean Difference Effect Sizes: Presenting Meta-Analytic Findings With Non-Researchers in Mind
Day, E., Trevino, S. D., Chinn, L. K., Schweer-Collins, M., Grant, S., & Tanner‐Smith, E. E. (2026) · Evaluation Review · doi:10.1177/0193841x261475375
A Humanoid Robot Morphology Does Not Always Lead To A Higher Perception Of Anthropomorphism And Trust
Söhngen, Y. N., Guerra, E., & Prilla, M. (2026) · Mensch und Computer (MuC) · doi:10.1145/3820253.3820279
Western Sandpiper ( Calidris mauri ) use of intertidal biofilm in freshwater and marine influenced mudflats during migratory stopover
Walters, S. H., Guglielmo, C. G., Drever, M. C., Flemming, S. A., Maftei, M., & Hobson, K. A. (2026) · Isotopes in Environmental and Health Studies · doi:10.1080/10256016.2026.2708365
Integrating Environmental Value Into Mobility‐as‐a‐Service: Behavioral Insights for Sustainable Mobility Strategy
Park, C., Kim, S., Shin, J., & Maeng, K. (2026) · Business Strategy and the Environment · doi:10.1002/bse.71427
Adaptive Multi-Graph Learning for Highway Monitoring and Resilience Assessment During Hurricane DorianPreprint
Liu, K., Hou, Q., & Peng, Z. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7472555
Getting Estimation Thinking into Educational Leadership: Challenges and Next Steps
Kline, R. B. (2026) · Educational Administration Quarterly · doi:10.1177/0013161x261487655
Sex and tenure influence rank within steep linear social hierarchies of Pacific morphotype Canada jays on Vancouver Island
Siemens, A. L., Strickland, D., Green, D. J., & Norris, D. R. (2026) · Behavioral Ecology and Sociobiology · doi:10.1007/s00265-026-03805-6
Quantifying parameter uncertainty in a three-parameter model of speech and voice adaptation using early- and late-window pitch-shift responses
Quinteros, N. F., Parra, J. A., Severino, L. A., Cortés, J. P., Alzamendi, G. A., Guenther, F. H., & Zañartu, M. (2026) · The Journal of the Acoustical Society of America · doi:10.1121/10.0046803
Comparative effectiveness of nature-based social prescribing to alleviate loneliness in Spain: a decision analysis informed by the RECETAS trial
Papon, V., Jahn, B., Sroczynski, G., Litt, J., Coll‐Planas, L., Masó-Aguado, M., Arvandi, M., Santamaria, J., Kuchin, I., Puntscher, S., et al. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1891830
Model-driven statistical crack assessment for cable-stayed bridges: a two-stage framework fusing monitoring and inspection data
Ge, X., Fu, Y., Wang, J., & Wang, Y. (2026) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-026-01125-x
Classical and Bayesian Parameter Estimation under Varying Sample Sizes and Data Conditions
Robiya, R. (2026) · Qubahan Journal of Social Science · doi:10.48161/qjss.v3n2a15

Authors: Hespanhol, L., Vallio, C. S., Costa, L. M., & Saragiotto, B. T.
Affiliations: Amsterdam UMC Location University of Amsterdam (NL)
Universidade Cidade de São Paulo (BR)
Corresponding author: Hespanhol, Luiz
ORCID: Hespanhol, Luiz
Vallio, Caio Sain
Saragiotto, Bruno T
Venue: Brazilian Journal of Physical Therapy
Publisher: Elsevier BV
Published: 2019-07
Volume: 23
Issue: 4
Pages: 290-301
ISSN: 1413-3555 (ISSN-L)
DOI: 10.1016/j.bjpt.2018.12.006
PMC: PMC6630113
OpenAlex: W2908045944
Cited by: 390
References: 25
Citations by year: 2026: 63 · 2025: 93 · 2024: 76 · 2023: 54 · 2022: 59 · 2021: 32 · 2020: 12 · 2019: 1
FWCI: 8.53
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Brazilian Journal of Physical Therapy (publishedVersion)
Europe PMC (PubMed Central) (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
Data Archiving and Networked Services (DANS) (submittedVersion)
Funders: Fundação de Amparo à Pesquisa do Estado de São Paulo (2016/09220-1, 2016/24217-7, 2017/11665-4)
Topics: Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords: confidence intervals, credible intervals, effect estimates, uncertainty quantification, frequentist interpretation, Bayesian interpretation, statistical significance, clinical decision-making, evidence-based practice
MeSH terms: Bayes Theorem, Humans, Uncertainty

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

Understanding and interpreting confidence and credible intervals around effect estimates

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks