{"id":"W6904510129","doi":"10.1371/journal.pone.0307306.s004","title":"Data extraction table.","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Frailty in Older Adults","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Grey literature; Transitional care; Data extraction; Health care; Inclusion (mineral); Scope (computer science); Best practice; Population","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.01821776,0.002588711,0.008722823,0.01996641,0.003008462,0.006413325,0.004063127,0.00316527,0.5326411],"category_scores_gemma":[0.1103984,0.002491535,0.004381923,0.02176633,0.001322282,0.003730728,0.003472641,0.00377797,0.08677591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007896137,"about_ca_system_score_gemma":0.03796756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02245194,"about_ca_topic_score_gemma":0.02290438,"domain_scores_codex":[0.9865008,0.00288247,0.005812541,0.001984818,0.002110356,0.0007090991],"domain_scores_gemma":[0.9194624,0.03991915,0.004527755,0.00626956,0.02805435,0.001766698],"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":[0.001213473,0.0001124442,0.0006520447,0.1512171,0.0002322508,0.0003011427,0.0007890288,0.0004235598,0.0002175435,0.006271577,0.7722851,0.06628474],"study_design_scores_gemma":[0.002251247,0.0001517043,0.002687168,0.06776223,0.0004887652,0.0001572454,0.001721049,0.0004124586,0.0004253729,0.01179745,0.9119871,0.0001581992],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007360608,0.002753275,0.004009834,0.001886542,0.001477401,0.07508412,0.8965629,0.000744582,0.01674531],"genre_scores_gemma":[0.005290103,0.007275328,0.0373053,0.003466136,0.0004491144,0.7273948,0.1902083,0.001017343,0.02759364],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4673589,"threshold_uncertainty_score":0.6666308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1493494936620308,"score_gpt":0.3780306326949879,"score_spread":0.2286811390329571,"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."}}