{"id":"W6969121874","doi":"10.5683/sp2/ebse5i","title":"Replication Data for: Development and validation of an algorithm to predict the treatment modality of burn wounds using thermographic scans","year":2020,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Modality (human–computer interaction); Replication (statistics); Patient data; Thermography","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":[0.004593925,0.00372778,0.001956497,0.002290342,0.001217266,0.002081631,0.005554688,0.003899528,0.01825123],"category_scores_gemma":[0.01550061,0.0008243235,0.003562165,0.001979828,0.0009790126,0.001216889,0.001965893,0.002552291,0.03260524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002011459,"about_ca_system_score_gemma":0.003761096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03502198,"about_ca_topic_score_gemma":0.06326167,"domain_scores_codex":[0.9968525,0.0006494022,0.0003093394,0.0008634482,0.001054357,0.000270995],"domain_scores_gemma":[0.9941329,0.001611279,0.000288554,0.001676045,0.002018488,0.0002727562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008273199,0.0003805526,0.004425209,0.00144566,0.0003502615,0.0001338649,0.00005859679,0.00487052,0.001271238,0.0005793624,0.9602907,0.0253667],"study_design_scores_gemma":[0.003778228,0.0006943866,0.03502062,0.001128928,0.0006630406,0.0007657763,0.000263713,0.02930265,0.01123327,0.006129375,0.9105636,0.0004564791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.004372348,0.0004894,0.003181305,0.0004447865,0.0004165391,0.0003503507,0.9844114,0.004152603,0.002181337],"genre_scores_gemma":[0.003769816,0.00008295525,0.00551413,0.0001618109,0.0000337079,0.0004562794,0.9884176,0.0001857933,0.001377844],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03502198,"threshold_uncertainty_score":0.06963629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1001529027645776,"score_gpt":0.3452656889020507,"score_spread":0.2451127861374731,"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."}}