{"id":"W4403997158","doi":"10.47909/ijsmc.137","title":"Health and medical informatics research: Identifying international collaboration patterns at the country and institution level","year":2024,"lang":"en","type":"article","venue":"Iberoamerican Journal of Science Measurement and Communication","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Institution; Health informatics; Informatics; Data science; Geography; Medicine; Political science; Computer science; Nursing; Public health","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.006604075,0.0003366292,0.0005551195,0.03837529,0.0007735179,0.004644294,0.0004751035,0.0005468696,0.003617761],"category_scores_gemma":[0.02958069,0.0001445977,0.0006044348,0.06494688,0.0006946606,0.004315829,0.002815578,0.0003277824,0.0005220904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001050967,"about_ca_system_score_gemma":0.001836873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002395054,"about_ca_topic_score_gemma":0.003899128,"domain_scores_codex":[0.9951918,0.001706753,0.0009705757,0.0007462251,0.001045407,0.0003392831],"domain_scores_gemma":[0.9600867,0.01980459,0.01441311,0.001423021,0.003175267,0.001097306],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008139128,0.00006060571,0.8884107,0.00160504,0.0004060996,0.0002723272,0.004423878,0.001212366,0.001115717,0.007370128,0.001810691,0.09323113],"study_design_scores_gemma":[0.00001708102,0.0001299539,0.9421659,0.001059509,0.0003390071,0.0008263718,0.01850441,0.004499913,0.001207806,0.00731495,0.02389303,0.00004214008],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9445238,0.008137785,0.0110479,0.001419851,0.00007064285,0.0002033053,0.007189605,0.000130098,0.02727708],"genre_scores_gemma":[0.9870946,0.001966971,0.007967253,0.00007893111,0.00006090436,0.0001616486,0.001997809,0.00002069575,0.0006511525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9933959,"threshold_uncertainty_score":0.03492612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1413070740599149,"score_gpt":0.4012548281072016,"score_spread":0.2599477540472868,"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."}}