Bibliographic record
Abstract
This thesis describes the application of health services research techniques to the study of toxicoepidemiology in Ontario using multiple administrative databases. From January 1st 1992 to December 31st 2001, we identified 80,888 hospital admissions in Ontario for which a poisoning (ICD-9 960.0 to 990.0) was the most responsible diagnosis. Of these, 43,965 (54.4%) were coded as self-inflicted, 46,970 (58.1%) involved women, and 5,793 (7.2%) involved children under age 6. About 5,708 patients died from poisoning, although only 884 of these were identified from hospital records. Acetaminophen poisoning (n = 12,738) was the most common diagnosis, followed by poisoning due to benzodiazepines (n = 9,368), antidepressants (n = 8,488) and salicylates (n = 3,344). Collectively, admissions for poisoning led to about 307,250 days in hospital. Examining specific poisonings, we found that hospitalization for iron poisoning in children less than age 3 was temporally associated with the birth of a sibling. Among elderly Ontarians treated with lithium, hospital admission for lithium toxicity was a relatively common occurrence and often followed a recognized drug interaction. Finally, hospitalization for toxic effects of digoxin, glyburide, and angiotensin converting enzyme (ACE) inhibitors was strongly associated with recent prescriptions for drugs known to provoke the toxicities of these agents. In addition to the specific findings outlined above, the results of this thesis demonstrate that the application of observational research methods to population-based healthcare databases is a feasible, efficient, and novel means of studying the epidemiology of poisoning.
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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 | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".