{"id":"W4410180988","doi":"10.1038/s41746-025-01594-2","title":"High performance with fewer labels using semi-weakly supervised learning for pulmonary embolism diagnosis","year":2025,"lang":"en","type":"article","venue":"npj Digital Medicine","topic":"Venous Thromboembolism Diagnosis and Management","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"North York General Hospital; St. Michael's Hospital; University of Toronto","funders":"","keywords":"Pulmonary embolism; Medicine; Artificial intelligence; Radiology; Pattern recognition (psychology); Machine learning; Computer science; Surgery","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":[0.004419989,0.001832309,0.001120298,0.001109319,0.0007066719,0.001552502,0.002036648,0.002086482,0.001556277],"category_scores_gemma":[0.01339025,0.0004766537,0.001014242,0.0005039347,0.0009754536,0.001881774,0.002189412,0.001895866,0.001532237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009599033,"about_ca_system_score_gemma":0.00172227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005780191,"about_ca_topic_score_gemma":0.008856907,"domain_scores_codex":[0.9971468,0.001289803,0.0001738214,0.0007340248,0.0004684011,0.000187258],"domain_scores_gemma":[0.9925067,0.004179655,0.0004494765,0.001126664,0.001467838,0.0002696935],"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":[0.00195543,0.001028802,0.04373731,0.0004957776,0.000593523,0.0006235512,0.0003800282,0.3386624,0.02425754,0.003346321,0.01303453,0.5718848],"study_design_scores_gemma":[0.00002819928,0.0001317157,0.001604776,0.00002251923,0.00004089911,0.00009897601,0.00003012409,0.9897773,0.004913467,0.002614391,0.0007188654,0.00001874326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2907498,0.001784489,0.6882572,0.001538464,0.0002017453,0.0002991263,0.0009771722,0.008672095,0.007520021],"genre_scores_gemma":[0.9119753,0.0001763325,0.08137888,0.0007158273,0.00009888301,0.0001069148,0.002185652,0.0001828908,0.00317928],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005780191,"threshold_uncertainty_score":0.02337539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01753628578119739,"score_gpt":0.2670570294513299,"score_spread":0.2495207436701325,"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."}}