{"id":"W6902016434","doi":"10.6084/m9.figshare.26677754","title":"Additional file 3 of Topic identification, selection, and prioritization for health technology assessment in selected countries: a mixed study design","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Delphi Technique in Research","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Prioritization; Health technology; Technology assessment; Health data; Key (lock); Multimethodology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006582849,0.000839234,0.0009919485,0.0031206,0.001066565,0.001571752,0.001456495,0.0009896038,0.8496362],"category_scores_gemma":[0.07229011,0.0006247397,0.0007158056,0.005074097,0.0002963131,0.001794283,0.001135281,0.0009960666,0.0669847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001960085,"about_ca_system_score_gemma":0.003860815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006731927,"about_ca_topic_score_gemma":0.01431658,"domain_scores_codex":[0.9974895,0.00110029,0.000517667,0.0002695526,0.0003916678,0.0002313969],"domain_scores_gemma":[0.9054878,0.07930509,0.003985035,0.002372393,0.007906734,0.0009430265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.000699859,0.0002336193,0.003427804,0.007110253,0.00006897967,0.0000641747,0.0006057904,0.0006530842,0.00009930296,0.001613133,0.9612951,0.02412895],"study_design_scores_gemma":[0.01525324,0.0008718964,0.05272529,0.01147162,0.0004276503,0.0003096239,0.004642045,0.004535829,0.001326441,0.01664465,0.8915486,0.0002431163],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.001006943,0.00002163276,0.0009908226,0.0003153373,0.00003266067,0.002215574,0.9927168,0.0002728496,0.002427348],"genre_scores_gemma":[0.04796503,0.0003425935,0.03692873,0.00192689,0.0001852504,0.1810107,0.6910799,0.001345785,0.0392152],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8496362,"threshold_uncertainty_score":0.2144756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1058491377725155,"score_gpt":0.4593269351841614,"score_spread":0.3534777974116459,"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."}}