{"id":"W3193585774","doi":"10.1016/j.jclinepi.2021.08.010","title":"The fragility index can be used for sample size calculations in clinical trials","year":2021,"lang":"en","type":"article","venue":"Journal of Clinical Epidemiology","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"National Institute of General Medical Sciences; National Institutes of Health; National Institute of Allergy and Infectious Diseases; Cornell University; Patient-Centered Outcomes Research Institute","keywords":"Index (typography); Sample size determination; Fragility; Statistics; Sample (material); Clinical trial; Medicine; Mathematics; Computer science; Internal medicine; Physics; Thermodynamics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","research_integrity"],"consensus_categories":["metaresearch","research_integrity"],"category_scores_codex":[0.546707,0.0003004608,0.009328758,0.00006358137,0.0001805708,0.00003092668,0.0007604477,0.001324048,0.0002997264],"category_scores_gemma":[0.9979534,0.0001818457,0.003867309,0.0003399364,0.001086194,0.00006222762,0.0002008461,0.002976818,0.000002968467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008378665,"about_ca_system_score_gemma":0.001393887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004217141,"about_ca_topic_score_gemma":0.0005776126,"domain_scores_codex":[0.7678398,0.1822576,0.04724333,0.0009232272,0.0006796693,0.00105632],"domain_scores_gemma":[0.006199587,0.9798992,0.01101191,0.001005443,0.00121956,0.0006642494],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00279312,0.001583911,0.584811,0.00009399781,0.0009492254,0.000058928,0.0000508483,0.00009655962,0.000008462099,0.307454,0.02202344,0.08007653],"study_design_scores_gemma":[0.004966051,0.0005258479,0.1607271,0.00007656523,0.000263361,0.0000163517,0.00006663128,0.001514695,0.000008308883,0.8155723,0.01611523,0.0001474917],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1169065,0.0002977865,0.7591231,0.1114329,0.01047624,0.001216714,0.0003729173,0.00003002587,0.0001437918],"genre_scores_gemma":[0.08150492,0.0008310563,0.9058282,0.007318762,0.004287972,0.00004416874,0.000002789423,0.00004363462,0.0001384665],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7976416,"threshold_uncertainty_score":0.9999725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9328040477738299,"score_gpt":0.7552170319925443,"score_spread":0.1775870157812856,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}