{"id":"W4408801012","doi":"10.1016/j.compmedimag.2025.102533","title":"A technology framework for distributed preoperative planning and medical training in deep brain stimulation","year":2025,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Neurological disorders and treatments","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Deep brain stimulation; Computer science; Stimulation; Brain stimulation; Physical medicine and rehabilitation; Training (meteorology); Medicine; Neuroscience; Artificial intelligence; Medical physics; Psychology; Pathology","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":[],"consensus_categories":[],"category_scores_codex":[0.0002697787,0.0001425939,0.0003661774,0.0002345476,0.0001083278,0.00002380624,0.00006589101,0.0002486758,0.00000772968],"category_scores_gemma":[0.002077409,0.0001119213,0.00003684302,0.0004001912,0.0003397363,0.000033139,0.00009056199,0.0004637808,1.029933e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000975091,"about_ca_system_score_gemma":0.0001005901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003865616,"about_ca_topic_score_gemma":0.000003746146,"domain_scores_codex":[0.9989052,0.00005010077,0.0002567054,0.0003389384,0.0002029307,0.0002461484],"domain_scores_gemma":[0.9987605,0.0008816727,0.00003497304,0.00009434499,0.00003621735,0.0001922823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006507025,0.0004059032,0.4232269,0.0002712391,0.0001721975,0.000733778,0.0013385,0.00001178316,0.00005228102,0.05759131,0.0003375335,0.5152079],"study_design_scores_gemma":[0.01204155,0.0004014398,0.13638,0.00198478,0.00009358054,0.00009272199,0.0004635258,0.5859711,0.00001521877,0.2614837,0.0008565917,0.0002157687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3727337,0.001797193,0.5817239,0.04316554,0.0001055546,0.0003612299,0.000005048221,0.0000842431,0.00002353225],"genre_scores_gemma":[0.9834802,0.0001428722,0.007478154,0.008747314,0.00003366635,0.00005305135,0.00005277182,0.000008078694,0.000003897278],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6107464,"threshold_uncertainty_score":0.456402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01885708152191072,"score_gpt":0.3350716657230132,"score_spread":0.3162145842011025,"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."}}