{"id":"W7100844863","doi":"","title":"Université de Montréal USE OF ANTI-INFECTIVE DRUGS DURING PREGNANCY: PREVALENCE, PREDICTORS AND THE RISK OF PRETERM BIRTH AND SMALL-FOR-","year":2016,"lang":"en","type":"article","venue":"","topic":"Pregnancy and Medication Impact","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"MEDLINE; Risk assessment; Population; Epidemiology; Risk factor","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":[0.0005571457,0.0003008896,0.000320998,0.001215646,0.001319486,0.001344325,0.000832401,0.0005500155,0.004830011],"category_scores_gemma":[0.004045883,0.0004288208,0.0004406447,0.00147321,0.0003250365,0.0003537479,0.0006195614,0.0009847465,0.0003605059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005598674,"about_ca_system_score_gemma":0.005705109,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.8105127,"about_ca_topic_score_gemma":0.81902,"domain_scores_codex":[0.9993318,0.0001127569,0.00004936418,0.0001388162,0.0002023698,0.0001648723],"domain_scores_gemma":[0.9982502,0.0002229478,0.0006009371,0.00005384436,0.0004143661,0.0004577303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006365043,0.00003509514,0.9924359,0.00003654931,0.00007312286,0.00005356322,0.0002285383,0.00003111808,0.0001319877,0.0001056086,0.001272736,0.005532024],"study_design_scores_gemma":[0.000005054259,0.00002802583,0.9983884,0.00004766249,0.00002532113,0.00007632245,0.0002159266,0.00009906555,0.00005574968,0.00001651957,0.001035367,0.00000656562],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9768823,0.009290328,0.000171589,0.002775125,0.0001292977,0.00003872333,0.005714315,0.00001742347,0.004980873],"genre_scores_gemma":[0.9919655,0.003724954,0.0002532739,0.0002246064,0.00006277404,0.00002296103,0.0008230708,0.000006453737,0.002916413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8105127,"threshold_uncertainty_score":0.3812065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007801148180446287,"score_gpt":0.2029030164138267,"score_spread":0.1951018682333804,"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."}}