{"id":"W2442688388","doi":"10.1097/bot.0000000000000463","title":"Bigger Data, Bigger Problems","year":2015,"lang":"en","type":"article","venue":"Journal of Orthopaedic Trauma","topic":"Hip and Femur Fractures","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Medicine; Merge (version control); Big data; Health care; Data quality; Certainty; Data science; MEDLINE; Actuarial science; Data mining; Operations management; Information retrieval; Computer science; Metric (unit)","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":[],"consensus_categories":[],"category_scores_codex":[0.1490583,0.002170557,0.003916157,0.008873028,0.004642824,0.02115439,0.005832648,0.01220583,0.0430623],"category_scores_gemma":[0.4362887,0.002145626,0.003479608,0.009383562,0.02114578,0.05342502,0.01654463,0.02264302,0.007349763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007177619,"about_ca_system_score_gemma":0.01185057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005239356,"about_ca_topic_score_gemma":0.003803105,"domain_scores_codex":[0.8346054,0.1038039,0.01535369,0.01781181,0.02519079,0.003234371],"domain_scores_gemma":[0.430107,0.4377776,0.02096069,0.06981256,0.03128289,0.01005924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004397802,0.0001695563,0.01087103,0.005206213,0.0008683251,0.0008894866,0.00470874,0.001291917,0.000397475,0.3753116,0.3866903,0.2131556],"study_design_scores_gemma":[0.0001424803,0.00007044434,0.00256626,0.00485268,0.000227288,0.001096805,0.003009602,0.001122576,0.0002465624,0.4379285,0.5486096,0.0001271573],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.005427583,0.06204179,0.05297893,0.8163556,0.02597108,0.0005279178,0.004229687,0.0009219897,0.03154535],"genre_scores_gemma":[0.1380181,0.07070912,0.2364914,0.4602495,0.06355577,0.002861246,0.007811139,0.003110078,0.01719378],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.1490583,"threshold_uncertainty_score":0.7883053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1134273422780063,"score_gpt":0.3324847487840265,"score_spread":0.2190574065060202,"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."}}