Population Trends in Substances Used in Deliberate Self-Poisoning Leading to Intensive Care Unit Admissions From 2000 to 2010
Bibliographic record
Abstract
OBJECTIVE: To examine population trends in serious intentional overdoses leading to admission to intensive care units (ICUs) in Winnipeg, Manitoba, Canada. METHOD: Participants consisted of 1,011 individuals presenting to any of the 11 ICUs in Winnipeg, Canada, with deliberate self-poisonings from January 2000 to December 2010. Eight categories of substances were created: poisons, over-the-counter medications, prescription medications, tricyclic antidepressants (TCAs), sedatives and antidepressants, anticonvulsants, lithium, and cocaine. Using the population of Winnipeg as the denominator, we conducted generalized linear model regression analyses using the Poisson distribution with log link to determine significance of linear trends in overdoses by substance over time. RESULTS: Women accounted for more presentations than men (57.8%), and the largest percentage of overdoses occurred among individuals in the 35- to 54-year age range. A large proportion of admissions were due to multiple overdoses, which accounted for 65.7% of ICU admissions. At the population level, multiple overdoses increased slightly over time (incidence rate ratio [IRR] = 1.02, P < .05), whereas use of poisons (IRR = 0.897, P < .01), over-the-counter medications (IRR = 0.910, P < .01), nonpsychotropic prescription medications (IRR = 0.913, P < .01), anticonvulsants (IRR = 0.880, P < .01), and TCAs (IRR = 0.920, P < .01) decreased over time. Overdoses did not change over time as a function of age or sex. However, severity of overdoses classified by length of stay increased over time (IRR = 1.08, P < .01). CONCLUSIONS: It is important for physicians to exercise vigilance while prescribing medication, including being aware of other medications their patients have access to.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".