{"id":"W6946158718","doi":"10.3389/fnut.2020.00024.s004","title":"Table_1_Evaluating Prevalence and Patterns of Prescribing Medications for Depression for Patients With Obesity Using Large Primary Care Data (Canadian Primary Care Sentinel Surveillance Network).pdf","year":2020,"lang":"en","type":"dataset","venue":"Figshare","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Depression (economics); Confidence interval; Obesity; Odds ratio; Logistic regression; Body mass index; Medical prescription; Medical record; Primary care","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001466615,0.0005551483,0.0007174278,0.006651055,0.001683478,0.001102454,0.00152914,0.0003698702,0.04356744],"category_scores_gemma":[0.01380105,0.0005569058,0.001323343,0.01614793,0.0002699925,0.000697758,0.000649553,0.0005612029,0.004793306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007510354,"about_ca_system_score_gemma":0.01886943,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9391508,"about_ca_topic_score_gemma":0.954859,"domain_scores_codex":[0.997973,0.0001440149,0.0002976335,0.0003117192,0.001029495,0.0002440008],"domain_scores_gemma":[0.9843183,0.001797772,0.001769339,0.0004759207,0.01090849,0.0007302578],"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":[0.00008987512,0.00004275919,0.3185592,0.002042102,0.0002546819,0.0000810754,0.0002285372,0.0004607855,0.0001911393,0.0004378493,0.6504294,0.02718272],"study_design_scores_gemma":[0.000090603,0.00002672724,0.9525389,0.0006385627,0.0001020448,0.0001085455,0.0004002686,0.0007161309,0.000179646,0.0001101565,0.04505225,0.00003616015],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.009066049,0.0002755756,0.0002348137,0.0002991513,0.00003551014,0.0003643908,0.9828137,0.0001671148,0.006743721],"genre_scores_gemma":[0.1333647,0.002259028,0.005857235,0.0009520815,0.0001006588,0.001557962,0.8481518,0.0001819459,0.007574601],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06084925,"threshold_uncertainty_score":0.1457477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05415730263598083,"score_gpt":0.2714326411360741,"score_spread":0.2172753385000933,"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."}}