{"id":"W7117357636","doi":"10.1016/j.metip.2025.100225","title":"Group-based trajectory modeling under non-random attrition: A sensitivity analysis and application to frailty trajectories","year":2025,"lang":"en","type":"article","venue":"Methods in Psychology","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Attrition; Trajectory; Dropout (neural networks); Monte Carlo method; Sensitivity (control systems); Constant (computer programming)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.05230466,0.001208197,0.001503959,0.001357754,0.0008340128,0.001625845,0.002059506,0.001937623,0.003529379],"category_scores_gemma":[0.1208302,0.0005262067,0.00361387,0.001480256,0.001411871,0.002405328,0.002866691,0.003486229,0.0002546534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002403071,"about_ca_system_score_gemma":0.002313461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02410712,"about_ca_topic_score_gemma":0.008952671,"domain_scores_codex":[0.9889057,0.009326578,0.0002305556,0.0008740558,0.0003754003,0.0002877257],"domain_scores_gemma":[0.8976896,0.08884185,0.003197057,0.007369893,0.002290377,0.0006112097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00164001,0.0002416871,0.0490358,0.0004452858,0.002267095,0.0003671705,0.0007891221,0.8747821,0.0006407697,0.02573415,0.002004501,0.04205225],"study_design_scores_gemma":[0.0001343395,0.0003833983,0.008823987,0.0001492525,0.0004713522,0.0001249437,0.0001810038,0.9546021,0.0005586449,0.03341963,0.001088431,0.00006294189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5199288,0.001618991,0.4716868,0.001836139,0.0001477871,0.0009684951,0.001205522,0.0005444232,0.002063085],"genre_scores_gemma":[0.9363351,0.0003182578,0.06112928,0.0002402068,0.00002941311,0.0005069305,0.0004956591,0.00006819366,0.0008767825],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.05230466,"threshold_uncertainty_score":0.2766168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04419897702253756,"score_gpt":0.4281515114161885,"score_spread":0.3839525343936509,"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."}}