{"id":"W2018118305","doi":"10.1371/journal.pone.0057687","title":"International Multispecialty Consensus on How to Evaluate Ultrasound Competence: A Delphi Consensus Survey","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Ultrasound in Clinical Applications","field":"Medicine","cited_by":206,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Wilson Centre; University of Toronto; University Health Network","funders":"Rigshospitalet; Gentofte Hospital","keywords":"Consensus conference; Delphi; Delphi method; Competence (human resources); Medicine; Medical physics; Computer science; Psychology; Internal medicine; Artificial intelligence","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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006860996,0.0002350625,0.0004918461,0.0001272611,0.0001164072,0.00009149973,0.0002777128,0.000145473,0.003197561],"category_scores_gemma":[0.01318588,0.0002121791,0.00009291741,0.0003049805,0.0003360219,0.00003002655,0.00006083449,0.0004255643,0.004103236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001717712,"about_ca_system_score_gemma":0.0001361365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003301454,"about_ca_topic_score_gemma":0.0001639943,"domain_scores_codex":[0.9973985,0.000185068,0.0005125267,0.0005653478,0.0009693688,0.0003691644],"domain_scores_gemma":[0.9910231,0.006307969,0.0001671356,0.0006773905,0.001357497,0.0004668924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001215758,0.01400203,0.4078285,0.000112566,0.002156913,0.00002713707,0.0005808108,0.00004810507,0.4685537,0.00139719,0.1001922,0.003885078],"study_design_scores_gemma":[0.003068166,0.0007613478,0.983731,0.0002979901,0.0002299177,0.00003822917,0.0001584377,0.001093141,0.006761018,0.0006035421,0.002846343,0.0004109255],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9583564,0.00001416283,0.0001043464,0.03003957,0.0001824731,0.001828842,0.0003351563,0.0001453338,0.00899366],"genre_scores_gemma":[0.9594908,0.00004069791,0.03367166,0.003117353,0.0004840808,0.0003115128,0.0002448912,0.0000412232,0.002597773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5759024,"threshold_uncertainty_score":0.9977136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1582123541736144,"score_gpt":0.3551814521628677,"score_spread":0.1969690979892532,"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."}}