{"id":"W2213130159","doi":"10.1515/cclm-2015-0867","title":"Developing GRADE outcome-based recommendations about diagnostic tests: a key role in laboratory medicine policies","year":2015,"lang":"en","type":"article","venue":"Clinical Chemistry and Laboratory Medicine (CCLM)","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Health Sciences Centre","funders":"","keywords":"Grading (engineering); Medicine; Health care; Test (biology); Medical laboratory; Process (computing); Quality (philosophy); Evidence-based medicine; Medical physics; Risk analysis (engineering); Computer science; Alternative medicine; Nursing; Pathology; Engineering","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":["metaresearch"],"category_scores_codex":[0.3542656,0.002143887,0.005664767,0.01569152,0.003519424,0.02228827,0.01652492,0.02368013,0.009120448],"category_scores_gemma":[0.7158265,0.002482192,0.006342428,0.009984668,0.00563867,0.01538698,0.01278245,0.02476113,0.01040608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02020716,"about_ca_system_score_gemma":0.08168337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02918421,"about_ca_topic_score_gemma":0.02371223,"domain_scores_codex":[0.536405,0.2612174,0.1116387,0.007145981,0.07529283,0.008300051],"domain_scores_gemma":[0.1948114,0.3815623,0.05969036,0.03541846,0.3081873,0.02033032],"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":[0.0005247816,0.0004014346,0.00640951,0.01454047,0.0008465884,0.0004140211,0.003064164,0.005001884,0.0006839889,0.08272342,0.5753399,0.3100499],"study_design_scores_gemma":[0.0005994143,0.0003082656,0.006380145,0.06912636,0.001005745,0.0002958807,0.002371275,0.005140977,0.001641234,0.1254634,0.7870559,0.0006112793],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.003670079,0.02878044,0.1169873,0.7662172,0.01391379,0.008121577,0.005923273,0.001885074,0.05450127],"genre_scores_gemma":[0.0769951,0.03121338,0.7065426,0.1433429,0.006756854,0.01415723,0.01036972,0.001552708,0.009069551],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6457344,"threshold_uncertainty_score":0.7963054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6817439745617168,"score_gpt":0.5714647626725438,"score_spread":0.110279211889173,"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."}}