{"id":"W4392300817","doi":"10.1136/bmjhci-2023-100780","title":"Bibliometric analysis of the 3-year trends (2018–2021) in literature on artificial intelligence in ophthalmology and vision sciences","year":2024,"lang":"en","type":"review","venue":"BMJ Health & Care Informatics","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact; University of Toronto; York University; McMaster University","funders":"","keywords":"Open access publishing; Library science; Geography; Optometry; Medicine; Computer science","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":["bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.001623127,0.00026304,0.001828851,0.1152001,0.00006310016,0.00009818531,0.0002470744,0.0002190174,0.0000199594],"category_scores_gemma":[0.0003933455,0.0001461874,0.0004524718,0.2605266,0.0001771705,0.00008549474,0.0001231219,0.000778247,0.000009479702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002227129,"about_ca_system_score_gemma":0.0006575696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001228487,"about_ca_topic_score_gemma":0.00003781754,"domain_scores_codex":[0.9968212,0.0002027157,0.001880992,0.0002336114,0.000557478,0.0003040397],"domain_scores_gemma":[0.9984367,0.0002772746,0.0006662321,0.000414432,0.0001088399,0.00009651427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.000008275741,0.00003018708,0.0006195079,0.0221639,0.0001134565,0.00002266929,0.002062974,0.00004439677,7.31363e-9,0.00009934756,0.0001635243,0.9746718],"study_design_scores_gemma":[0.0006592826,0.006017209,0.02403877,0.5329676,0.02397969,0.001657978,0.02651673,0.05951937,0.000003469292,0.0003649313,0.3219914,0.002283608],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001811698,0.9957089,0.00001127265,0.0005260155,0.000189083,0.0004159368,0.00007741136,0.000007385675,0.001252292],"genre_scores_gemma":[0.007336468,0.9917088,0.0005317969,0.0001509696,0.00005587033,0.00002102326,0.0001239092,0.00001154977,0.0000596328],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9723881,"threshold_uncertainty_score":0.8948284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0888833516075595,"score_gpt":0.5040815365056307,"score_spread":0.4151981848980712,"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."}}