{"id":"W2757536903","doi":"10.1038/nmeth.4457","title":"The inconvenience of data of convenience: computational research beyond post-mortem analyses","year":2017,"lang":"en","type":"letter","venue":"Nature Methods","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"SickKids Foundation; University of Toronto","funders":"","keywords":"Computer science; Data science; Computational biology; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02902262,0.0004552664,0.001681889,0.0009252455,0.001898904,0.004143974,0.002572623,0.01969285,0.00609899],"category_scores_gemma":[0.192367,0.0004909447,0.0007972745,0.001101093,0.01323792,0.008531995,0.003170516,0.03471671,0.00575186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002102559,"about_ca_system_score_gemma":0.002873315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001092787,"about_ca_topic_score_gemma":0.001939503,"domain_scores_codex":[0.9852426,0.008833163,0.001346373,0.001357276,0.002926267,0.0002943798],"domain_scores_gemma":[0.6838556,0.2792747,0.005632472,0.01407061,0.01357602,0.003590566],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002622335,0.00003860265,0.001560975,0.0003544657,0.00005889593,0.001430037,0.0003929851,0.0002901558,0.0002405839,0.0740959,0.7949492,0.126326],"study_design_scores_gemma":[0.0001606559,0.00006738302,0.001219464,0.001248911,0.00004311357,0.003443459,0.0004217546,0.001750284,0.0005305954,0.61365,0.3773659,0.00009854264],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.000640505,0.005595569,0.003871006,0.9795435,0.008127998,0.00001078518,0.0000830879,0.00003924098,0.002088274],"genre_scores_gemma":[0.02436475,0.00914192,0.008354805,0.8373952,0.1174772,0.0001421512,0.00009285592,0.000161508,0.002869668],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.9709774,"threshold_uncertainty_score":0.1534881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3262454615031201,"score_gpt":0.6153864163255818,"score_spread":0.2891409548224617,"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."}}