966 Tough pill to swallow: a retrospective review of poison-related hospital visits in Alberta
Bibliographic record
Abstract
Background Little is known about the events and circumstances that contribute to poison-related incidents and mortality in Alberta. Poisoning events may take on any of three forms of exposure: Unintentional (including iatrogenesis – complications resulting from medical care), Experimental (including suspected suicidality), and Intentional. The objective of the current study was to review and analyse patient records from 18 hospital sites to ascertain trends in morbidity, mortality, and healthcare utilisation for medical incidents related to poisonings in Alberta. Methods A retrospective analysis of health services records involving poisoning events was conducted for the 2010 calendar year. The Emergency Department (ED) and In-Patient (IP) data were analysed as separate event-sets to compare and contrast circumstances and conditions, and subsequent courses of action taken. Protocol filters yielded a total of 1,729 records used in the final analysis; 1,360 ED records and 369 IP records. The data were summarised using descriptive statistics and presented as frequency tables and graphs, and where appropriate as means. Where inferential statistics are used, the level for significance was set at p < 0.05. Results The majority of records indicated intentional poisonings (i.e. voluntary self-harm). Females were implicated in 56.1% of all ED poison visits, while their male counterparts were fewer at 43.9%. The majority of cases involved adults aged 22–60 years. Young adults aged 17–21 years and toddlers aged 1–5 years were the next most represented groups. Overall, the highest number of poisonings was attributed to prescription medications. Females consumed predominantly prescription medications while males consumed primarily recreational substances. Conclusions Intentional poisoning were found overall to be higher than unintentional poisonings. Exposures varied between age groups and other demographic factors. Rural populations exhibit a unique set of circumstances and outcomes given relative isolation from major centres. Poisonings are complex events that span financial, socio-cultural, and healthcare domains. These events are a considerable societal and financial burden in Alberta – both intentional and unintentional. Many poisonings that appear benign on the surface may have implications beyond the acute presentation of symptoms.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".