{"id":"W2089082035","doi":"10.1002/bimj.200710419","title":"Generalized Log‐Rank Tests for Partly Interval‐Censored Failure Time Data","year":2008,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Interval (graph theory); Statistics; Mathematics; Log-rank test; Rank (graph theory); Data set; Survival analysis; Confidence interval; Class (philosophy); Accelerated failure time model; Set (abstract data type); Computer science; Combinatorics; Artificial intelligence","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"],"consensus_categories":[],"category_scores_codex":[0.001063485,0.000207406,0.0005212872,0.0005272754,0.0002445806,0.000101718,0.0008215692,0.0001511583,0.000761588],"category_scores_gemma":[0.02043503,0.0001495567,0.0001540847,0.001258838,0.000145354,0.0001498741,0.0002107677,0.0003142629,0.00009279516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007031123,"about_ca_system_score_gemma":0.00009452437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003985564,"about_ca_topic_score_gemma":6.161364e-7,"domain_scores_codex":[0.9978098,0.0002266783,0.0006863156,0.0003325395,0.0004782495,0.0004664098],"domain_scores_gemma":[0.9949798,0.003535357,0.0002559517,0.0005435614,0.0003038772,0.0003814306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002544653,0.0007122225,0.0008648155,0.00008417578,0.0002654112,0.000219245,0.0001018221,7.828808e-7,0.008322114,0.03035739,0.8775726,0.08124495],"study_design_scores_gemma":[0.01105589,0.002733925,0.006274608,0.0003037069,0.0006095492,0.004810196,0.0001161172,0.04628287,0.004605887,0.643647,0.2775907,0.001969638],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04068069,0.0003959442,0.9557298,0.00138827,0.0004431899,0.0003396973,0.000533345,0.00008012437,0.0004089618],"genre_scores_gemma":[0.03160949,0.00009567948,0.966598,0.0002460938,0.0007945977,0.000008996222,0.00003287659,0.0000361064,0.0005781164],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6132895,"threshold_uncertainty_score":0.9878163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3234324305312045,"score_gpt":0.4405000208752892,"score_spread":0.1170675903440848,"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."}}