{"id":"W2106169817","doi":"10.1002/sim.4394","title":"A longitudinal model for magnetic resonance imaging lesion count data in multiple sclerosis patients","year":2011,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Magnetic resonance imaging; Nonparametric statistics; Computer science; Parametric statistics; Statistical power; Markov chain; Statistics; Data mining; Medical physics; Artificial intelligence; Pattern recognition (psychology); Machine learning; Medicine; Mathematics; Radiology","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.001061223,0.0002372239,0.0005645492,0.0003311269,0.00009029452,0.000007426318,0.00036197,0.00005473195,0.00008343032],"category_scores_gemma":[0.006923093,0.0001967761,0.00001972,0.0003093598,0.0004350552,0.00011929,0.0003245936,0.0003210748,0.00000638513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002230345,"about_ca_system_score_gemma":0.0001112999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001206992,"about_ca_topic_score_gemma":0.002575554,"domain_scores_codex":[0.9972868,0.00005844295,0.0006710692,0.0006501778,0.0007433855,0.0005901043],"domain_scores_gemma":[0.9979414,0.0006412365,0.0001025526,0.0007939855,0.0003604101,0.0001603532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000974882,0.0005073508,0.8923091,0.0003157257,0.000009447709,0.0000409155,0.002115479,0.00001391443,0.0003399171,0.0002372982,0.02018612,0.08294982],"study_design_scores_gemma":[0.006924229,0.0002974057,0.6133537,0.0008826634,0.00002966338,9.847059e-7,0.00017178,0.3774036,0.00001419918,0.0004764046,0.0003291201,0.0001162516],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.393536,0.02231471,0.5500027,0.003494308,0.001426614,0.01301642,0.01239043,0.0002335839,0.00358527],"genre_scores_gemma":[0.8916699,0.001855172,0.1051566,0.0002237523,0.00009181939,0.0001680022,0.0006101778,0.00004341758,0.0001811875],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.498134,"threshold_uncertainty_score":0.8288088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2889380930924304,"score_gpt":0.3821583364372672,"score_spread":0.0932202433448368,"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."}}