{"id":"W3205604716","doi":"10.1177/23333928211047024","title":"Trajectories of Follow-up Compliance in a Fracture Liaison Service and Their Predictors: A Longitudinal Group-Based Trajectory Analysis","year":2021,"lang":"en","type":"article","venue":"Health Services Research and Managerial Epidemiology","topic":"Bone health and osteoporosis research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sanofi (Canada); McGill University; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean; Université de Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Hôpital du Sacré-Cœur de Montréal","funders":"Fonds de Recherche du Québec - Santé; Réseau Québécois de Recherche sur les Médicaments; Eli Lilly and Company","keywords":"Polypharmacy; Multinomial logistic regression; Medicine; Referral; Trajectory; Osteoporosis; Logistic regression; Physical therapy; Identification (biology); Family medicine; Internal medicine; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005543363,0.0003433864,0.0003788493,0.001445221,0.0007482418,0.0008767624,0.0006684235,0.0004870721,0.002298355],"category_scores_gemma":[0.01068213,0.0002504292,0.0009865547,0.001682244,0.0002991274,0.0007727134,0.00109589,0.0008647986,0.000300262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006560502,"about_ca_system_score_gemma":0.001112265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02045605,"about_ca_topic_score_gemma":0.01171862,"domain_scores_codex":[0.9988471,0.0006551173,0.00006796703,0.0001762875,0.0001011024,0.000152309],"domain_scores_gemma":[0.9949621,0.001893949,0.001357354,0.0007773667,0.0004752024,0.0005341382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002279423,0.00008171148,0.995869,0.00000693425,0.00009358463,0.00002272155,0.0002161107,0.0005574941,0.00006197091,0.00007659995,0.0001394616,0.002646551],"study_design_scores_gemma":[0.00003610739,0.000568044,0.9703215,0.00003219664,0.0001403795,0.0001198539,0.001174102,0.02646728,0.0001602948,0.0005353196,0.0004256352,0.00001920926],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978765,0.00004848784,0.001388728,0.00007216115,0.000003767975,0.00003459095,0.0004592364,0.00001110543,0.000105351],"genre_scores_gemma":[0.997528,0.00003808569,0.00123263,0.000008613143,0.000003096724,0.00005792628,0.0009436648,0.000005159719,0.0001828795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02045605,"threshold_uncertainty_score":0.04067397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1109918860054368,"score_gpt":0.4241805113689409,"score_spread":0.3131886253635041,"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."}}