{"id":"W4380577731","doi":"10.1038/s41598-023-36702-3","title":"Tool-tissue force segmentation and pattern recognition for evaluating neurosurgical performance","year":2023,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Artificial intelligence; Segmentation; Machine learning; Task (project management); Pattern recognition (psychology)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008939783,0.001068821,0.0004447931,0.001407378,0.0002147718,0.0007695397,0.0004653019,0.001006859,0.001600472],"category_scores_gemma":[0.002418676,0.0001745568,0.0006077318,0.0007127812,0.0002762011,0.000575909,0.0003475469,0.0004901565,0.0008204706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005793717,"about_ca_system_score_gemma":0.0005048616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005231489,"about_ca_topic_score_gemma":0.005754119,"domain_scores_codex":[0.9995372,0.00007633212,0.00004327618,0.0001458241,0.000142704,0.00005464437],"domain_scores_gemma":[0.9991369,0.0003673602,0.0001662937,0.00007305477,0.000214191,0.00004237984],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005762294,0.0008041314,0.07033678,0.0002297974,0.0003274748,0.0002280209,0.0001776149,0.290871,0.05018362,0.0009785554,0.004297585,0.5809891],"study_design_scores_gemma":[0.000006888353,0.0004340711,0.02964344,0.00003726978,0.00003852512,0.0001275852,0.00006602419,0.9515775,0.0164799,0.0006431498,0.0009200519,0.00002563076],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6160548,0.001186705,0.3708505,0.0003784625,0.000137697,0.0002508725,0.001649979,0.003187217,0.006303718],"genre_scores_gemma":[0.9499178,0.0002515285,0.04607638,0.00008885046,0.0000288425,0.0001385231,0.001181029,0.00005228761,0.002264788],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005231489,"threshold_uncertainty_score":0.01040208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1010484377975421,"score_gpt":0.3718285398419509,"score_spread":0.2707801020444088,"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."}}