Measuring Levels of Comorbidity in Drug User* Emergency Patients Treated in Ontario Hospitals
Bibliographic record
Abstract
We examined the nature and extent of health problems in drug user patients initially treated in emergency departments and who were subsequently admitted to all hospitals in Ontario, using data from the Hospital Medical Records Institute (HMRI). The modified standardized morbidity ratio (modified SMR) is introduced to improve the measurement and visual display of reduced morbidity as well as excess morbidity. During 1985-86, about 75% of drug user patients entered hospital through the emergency department. There were 5077 emergency patients with primary drug use-related diagnoses and 9827 with secondary drug use-related diagnoses. Cases with poisoning diagnosis made up over 80% of all drug use cases treated in emergency departments. Cases with non-dependent abuse of drugs accounted for 8-12% of emergency drug user patients, whereas those with drug dependence accounted for about 3% of emergency drug user patients. These patients had more than three times the comorbidity experience of all hospital patients. They had excess comorbidity due to mental disorders, infectious and parasitic disorders, and injury and poisoning diagnoses. However, they had reduced comorbidity due to complications of pregnancy, childbirth, and the puerperium and from congenital anomalies and conditions originating in the perinatal period. Cocaine patients were more likely to have infectious parasitic diseases and diseases of the skin and subcutaneous tissue, while amphetamine patients were more likely to have diseases of the digestive system and of the musculo-skeletal system and connective tissue.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".