{"id":"W3112995881","doi":"10.1093/neuonc/noaa222.684","title":"QOL-22. MACHINE-LEARNING INFERENCE MAY PREDICT QUALITY OF LIFE SUBGROUPS OF ADAMANTINOMATOUS CRANIOPHARYNGIOMA","year":2020,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Pituitary Gland Disorders and Treatments","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Craniopharyngioma; Medicine; Quality of life (healthcare); Categorical variable; Artificial intelligence; Machine learning; Pediatrics; Surgery; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.00113961,0.0005116442,0.0002865693,0.0008109555,0.0001525715,0.000674177,0.0002623959,0.0003711039,0.00146537],"category_scores_gemma":[0.005363459,0.0001207956,0.0005744545,0.0003773558,0.0001588187,0.0003207912,0.0003778905,0.0005548875,0.0004281123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005896664,"about_ca_system_score_gemma":0.000454915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004437922,"about_ca_topic_score_gemma":0.004944819,"domain_scores_codex":[0.9997289,0.00008986045,0.0000328582,0.00007477945,0.00003538984,0.00003818585],"domain_scores_gemma":[0.9987509,0.0005981291,0.0002953598,0.00009034172,0.000168637,0.00009661612],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006529302,0.0002765237,0.8742229,0.00006414881,0.0002433351,0.0001441542,0.00009384175,0.0341715,0.002557891,0.0001898442,0.001816792,0.08556627],"study_design_scores_gemma":[0.00004588407,0.0004356376,0.4211043,0.00004905705,0.0001135502,0.0003142258,0.0001795233,0.5710834,0.003735963,0.001901018,0.001006943,0.00003044581],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9876034,0.0002294843,0.009206795,0.0002401184,0.0000199093,0.00004576804,0.001766623,0.0002409948,0.0006470887],"genre_scores_gemma":[0.9949249,0.00003542753,0.003531537,0.00002815033,0.000009100469,0.00003064348,0.001259745,0.000007672035,0.0001728614],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004437922,"threshold_uncertainty_score":0.00882417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04000410185333333,"score_gpt":0.3170887821119712,"score_spread":0.2770846802586379,"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."}}