{"meta":{"query_hash":"5f806d8889a7","filters":{"venue":"International Journal of Digital Health"},"cohort_total":1,"direct_labels_cover":0,"predictions_cover":1,"exported":1,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/5f806d8889a7","api":"https://metacan.xera.ac/api/v1/cohort?venue=International+Journal+of+Digital+Health"},"results":[{"id":"W3134332400","doi":"10.29337/ijdh.24","title":"Digital Health, Big Data and Connectivity: 5G and Beyond for Patient-Centred Care","year":2021,"lang":"en","type":"article","venue":"International Journal of Digital Health","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Big data; Digital health; Health care; Data science; Computer science; Psychology; Political science; Data mining","score_opus":0.1846319181436164,"score_gpt":0.46019321696863497,"score_spread":0.27556129882501856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134332400","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038327717,0.8178304,0.017363783,0.10922754,0.007624488,0.000053806085,0.00022798179,0.00015010744,0.043689135],"genre_scores_gemma":[0.06808675,0.87908685,0.010422402,0.023809686,0.012740393,0.00006244678,0.00014869495,0.000033830263,0.005608899],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987588,0.00054984033,0.0000762686,0.00011103568,0.0003964799,0.000107576896],"domain_scores_gemma":[0.99679416,0.0022621239,0.00018259828,0.00012269571,0.00042121124,0.0002171067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001937979,0.00040448093,0.00047469678,0.0015401343,0.00058089674,0.0035607796,0.00060289464,0.0023370353,0.005016268],"category_scores_gemma":[0.002749342,0.00015436558,0.0005595034,0.0018241735,0.0018376056,0.004590206,0.0020522166,0.0030036587,0.00081822474],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009120717,0.000052011717,0.001549587,0.005467548,0.0001007836,0.00042915152,0.00067371613,0.0019166915,0.0018320333,0.38680577,0.08948186,0.51159966],"study_design_scores_gemma":[0.000008305608,0.000087211,0.0016417927,0.0060536694,0.000049034068,0.00082635495,0.0009042092,0.0020927705,0.000582637,0.12331194,0.86439466,0.000047411413],"about_ca_topic_score_codex":0.0013438648,"about_ca_topic_score_gemma":0.0013960606,"teacher_disagreement_score":0.005016268,"about_ca_system_score_codex":0.0013278319,"about_ca_system_score_gemma":0.0016551205,"threshold_uncertainty_score":0.016781092},"labels":[],"label_agreement":null}]}