Impact of acetaminophen product labelling changes in Canada on hospital admissions for accidental acetaminophen overdose: a population-based study
Bibliographic record
Abstract
<h3>Background:</h3> Accidental acetaminophen overdoses are associated with substantial morbidity and health care costs. In Canada, updated labelling standards were implemented in October 2009 and September 2016, with the intent of communicating risks of overdose and facilitating product identification and safe use, respectively. Full compliance with the 2016 standards was expected by March 2018. We sought to explore whether these changes affected rates of hospital admission for accidental acetaminophen overdose. <h3>Methods:</h3> We conducted a population-based study of hospital admissions for accidental acetaminophen overdose in 9 Canadian provinces and 3 Canadian territories between Apr. 1, 2004, and Mar. 31, 2020. We used interventional autoregressive integrated moving average (ARIMA) models to evaluate the impact of the updated labelling standards on rates of hospital admission for accidental acetaminophen overdose. In secondary analyses, we studied intensive care unit (ICU) admissions and hospital admissions for accidental acetaminophen overdose involving opioids. <h3>Results:</h3> Monthly rates of hospital admission for accidental acetaminophen overdose were essentially unchanged over the study period (0.21 and 0.22 cases per 100 000 population in April 2004 and March 2020, respectively). We found no association between changing labelling standards and trends in rates of hospital admission for accidental acetaminophen overdose (October 2009 <i>p</i> = 0.2, September 2016 <i>p</i> = 0.7 and March 2018 <i>p</i> = 0.2). Similarly, labelling changes did not have an impact on admissions involving ICU admission and concomitant opioid poisoning. <h3>Interpretation:</h3> Modifications to product labels did not reduce the rate of acetaminophen-related harm. Additional measures to reduce the burden of accidental acetaminophen overdose are required.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".