{"id":"W4398181987","doi":"10.1007/s11136-024-03672-6","title":"Extracting big data from the internet to support the development of a new patient-reported outcome measure for breast implant illness: a proof of concept study","year":2024,"lang":"en","type":"article","venue":"Quality of Life Research","topic":"Health Literacy and Information Accessibility","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Measure (data warehouse); The Internet; Big data; Quality of Life Research; Proof of concept; Public health; Patient-reported outcome; Medicine; Outcome (game theory); Data science; Quality of life (healthcare); Psychology; Computer science; Data mining; World Wide Web; Nursing; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.02677283,0.0009741505,0.001176099,0.001678856,0.0006450078,0.003015486,0.001617151,0.001207987,0.002447076],"category_scores_gemma":[0.07123985,0.0004550564,0.001704959,0.001564732,0.0008419974,0.002603523,0.00230015,0.001759402,0.0006846205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005564759,"about_ca_system_score_gemma":0.003117473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001379575,"about_ca_topic_score_gemma":0.001784841,"domain_scores_codex":[0.9866426,0.008349253,0.001045841,0.001022557,0.002495855,0.0004439268],"domain_scores_gemma":[0.913711,0.06529587,0.004641727,0.005868764,0.009067732,0.001414913],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0144213,0.02540208,0.3091978,0.008490574,0.004872795,0.0005003666,0.002640316,0.009042199,0.0284729,0.006170636,0.02329864,0.5674904],"study_design_scores_gemma":[0.0148265,0.05441463,0.472183,0.005773015,0.01201547,0.002346921,0.01071273,0.2203609,0.09655041,0.0344739,0.07532039,0.001022157],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8095827,0.002868758,0.135532,0.006180208,0.0009166063,0.01494318,0.0246434,0.001181478,0.004151621],"genre_scores_gemma":[0.6741447,0.001408473,0.2929096,0.002193695,0.0003100775,0.008968918,0.01912377,0.000160562,0.0007801856],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02677283,"threshold_uncertainty_score":0.1415899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.718347652069068,"score_gpt":0.6161766084589263,"score_spread":0.1021710436101417,"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."}}