{"id":"W4200239034","doi":"10.1136/bmjebm-2021-111850","title":"Users' Guides to the Medical Literature series on social media (part 2): how to appraise studies using data from platforms","year":2021,"lang":"en","type":"article","venue":"BMJ evidence-based medicine","topic":"Social Media in Health Education","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University; Impact; Université de Montréal","funders":"","keywords":"Series (stratigraphy); Social media; Data science; Computer science; World Wide Web; Information retrieval; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["metaresearch"],"domain":"methods","study_design":"not_applicable","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["metaresearch"],"domain":"evaluation","study_design":"not_applicable","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts"],"consensus_categories":[],"category_scores_codex":[0.00721352,0.0003153873,0.0006851408,0.0001785272,0.001403164,0.0001705961,0.001410884,0.0003324189,0.0005821879],"category_scores_gemma":[0.3462951,0.0002122637,0.00007206599,0.002128395,0.0009159776,0.000788226,0.0003323042,0.0005587315,0.00006624503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005187217,"about_ca_system_score_gemma":0.004305925,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001754204,"about_ca_topic_score_gemma":0.02000595,"domain_scores_codex":[0.993498,0.0008360172,0.0006949655,0.0008393913,0.00340645,0.0007252171],"domain_scores_gemma":[0.9697146,0.02635018,0.0003392149,0.001261963,0.001306258,0.001027829],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004175834,0.0001054567,0.002905117,0.0001981872,0.0001209211,0.0002138939,0.2661308,0.00001932042,0.000118354,0.002261175,0.6976081,0.02990112],"study_design_scores_gemma":[0.000980814,0.0002683267,0.009163463,0.01623545,0.0002672029,0.000008951142,0.2094912,0.00008136846,0.0003476654,0.001464947,0.7610567,0.0006338917],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1957763,0.009285205,0.0001994902,0.774536,0.01798259,0.001894523,0.0001139147,0.0001164594,0.00009556179],"genre_scores_gemma":[0.5461551,0.01468484,0.01872779,0.1508142,0.2633271,0.003737638,0.001498488,0.0002319345,0.0008229726],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.6237218,"threshold_uncertainty_score":0.9998969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.64309403782733,"score_gpt":0.5438279267303098,"score_spread":0.09926611109702022,"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."}}