{"id":"W3110361800","doi":"10.1016/j.ypmed.2020.106331","title":"Impact of COVID-19 lockdown policy on homicide, suicide, and motor vehicle deaths in Peru","year":2020,"lang":"en","type":"article","venue":"Preventive Medicine","topic":"COVID-19 and healthcare impacts","field":"Medicine","cited_by":211,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Medicine; Homicide; Poison control; Injury prevention; Suicide prevention; Demography; Accidental; Occupational safety and health; Suicide methods; Coronavirus disease 2019 (COVID-19); Pandemic; Medical emergency; Environmental health; Disease; Suicide rates; Infectious disease (medical specialty); Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005101009,0.0002757695,0.0008606952,0.0004797812,0.00005122526,0.000005081894,0.0001148924,0.0001179656,0.000409075],"category_scores_gemma":[0.006413104,0.0002007288,0.0001381066,0.0006927407,0.0002455334,0.00007191586,0.00007677999,0.0003874635,0.0000151011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006025048,"about_ca_system_score_gemma":0.001827081,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01407419,"about_ca_topic_score_gemma":0.0002083839,"domain_scores_codex":[0.9979736,0.0001662621,0.0005674061,0.0004353147,0.0004157201,0.0004416564],"domain_scores_gemma":[0.9975747,0.0005634048,0.0001903046,0.0002859399,0.000116959,0.001268659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003443756,0.0002887172,0.9514339,0.001113885,0.0001164489,0.0002049512,0.007294021,0.00002404883,0.02137794,0.0004457259,0.002201559,0.01205503],"study_design_scores_gemma":[0.007287872,0.006978554,0.9821154,0.0006244051,0.00007436743,0.00002872036,0.0003832078,0.0003090232,0.0005185155,0.0004646779,0.001061311,0.0001538726],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.928353,0.001786461,0.0001758634,0.06779373,0.00003990968,0.001017635,0.00004893008,0.00004834688,0.0007361053],"genre_scores_gemma":[0.9832807,0.0005573227,0.00004835118,0.01536478,0.0004797756,0.00001665198,0.00002528733,0.00003129907,0.0001957884],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05492773,"threshold_uncertainty_score":0.9924912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09159628942968194,"score_gpt":0.447721648745722,"score_spread":0.3561253593160401,"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."}}