Adverse events related to medications identified by a Canadian poison centre.
Bibliographic record
Abstract
BACKGROUND: Poison centres are an underutilized source of information on adverse events related to medications, including therapeutic errors and adverse drug reactions. OBJECTIVE: To demonstrate the feasibility of using a poison centres' electronic data to identify and describe adverse events related to medications. METHODS: This one-year, retrospective cross-sectional pilot study was conducted at one Canadian Poison Centre. All records from the IWK Regional Poison Centre database in Nova Scotia between November 1, 2007 and October 31, 2008 for unintentional exposures were abstracted for a descriptive data analysis. RESULTS: An issue related to use of a medication was the main reason for 1,525 (32.5%) of 4,697 eligible calls. Of the 1,525 calls, 970 (63.6%) were coded as 'unintentional-general.' There were 470 (30.8%) calls for unintentional therapeutic errors and 61 (4.0%) for adverse drug reactions. The majority of calls involving medications were judged to have resulted in minimal or no toxic effect (78.4%). However, 3.3% of calls involving adverse drug reactions resulted in admission to a critical care unit (n=2). Approximately 1% of calls involving unintentional therapeutic errors resulted in admission to hospital (n=6). CONCLUSIONS: Calls to poison centres provide a potentially valuable source of information on adverse events related to medications that are likely not reported elsewhere. Establishment of a mechanism to routinely share information from all Canadian poison centres with relevant national drug safety programs (e.g., MedEffect™ Canada) will provide a supplementary source of information and contribute to building capacity for detection of sentinel events and pharmacosurveillance.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".