{"id":"W2174969218","doi":"10.1200/jco.2015.63.0152","title":"Medical Education and Training: Building In-Country Capacity at All Levels","year":2015,"lang":"en","type":"review","venue":"Journal of Clinical Oncology","topic":"Global Health and Surgery","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"National Cancer Institute","keywords":"Medicine; Workforce; Capacity building; Referral; Developing country; Training (meteorology); Health care; Leverage (statistics); Public health; Workforce development; Nursing; Medical education; Economic growth","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":["metaresearch","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.01903858,0.0002460362,0.006219664,0.0002788841,0.00002444097,0.000005860592,0.0001781371,0.002055635,0.0001438028],"category_scores_gemma":[0.02819841,0.000164208,0.0005997908,0.0001915647,0.0002487172,0.00006772104,0.00008127293,0.003022642,0.0000135786],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009625229,"about_ca_system_score_gemma":0.04370158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002909532,"about_ca_topic_score_gemma":0.00006429978,"domain_scores_codex":[0.9924652,0.001132792,0.004950643,0.000265973,0.000741927,0.0004434568],"domain_scores_gemma":[0.9901229,0.004093812,0.002934959,0.0001753523,0.0003808307,0.002292189],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002195452,0.000663175,0.001191259,0.002654988,0.0001244773,0.0006181891,0.0000575514,1.917419e-8,1.617259e-8,0.0001209596,0.0196221,0.9747277],"study_design_scores_gemma":[0.001703148,0.001516926,0.001981244,0.008393274,0.0006712484,0.009485708,0.00003313561,0.000003700543,6.131165e-9,0.0002894047,0.9758059,0.0001163008],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.008888129,0.9798849,0.000003894517,0.003113025,0.004692021,0.0002857317,0.000006858515,0.000005403892,0.003120021],"genre_scores_gemma":[0.0006912356,0.9875694,0.001150706,0.007338404,0.003062475,0.000005867037,0.000008656183,0.00002153761,0.0001517213],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9746114,"threshold_uncertainty_score":0.9992774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4973004834589068,"score_gpt":0.6024003784765894,"score_spread":0.1050998950176827,"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."}}