Practice Trends in the Use of Extracorporeal Treatments for Poisoning in Four Countries
Bibliographic record
Abstract
Extracorporeal treatments (ECTRs) such as hemodialysis (HD), enhance the elimination of a small number of toxins. Changes in overdose trends, prescribing practices, antidotes, and dialysis techniques may alter the indications and rates of ECTR use over time. This study analyzed trends in ECTR for poisonings in four countries. A retrospective study of national poison center databases from the United States, Denmark, United Kingdom, and five regional databases within Canada was performed. All cases of patients receiving an ECTR were included. ECTR cases were totalled annually and reported as annual rates per 100,000 exposures with stratification per types of ECTR and toxins. The data collection varied by countries. United States, 1985-2014; United Kingdom, 2011-2013; Denmark, 2005-2014, and regions of Canada as follows: Alberta, 1991-2015; Saskatchewan, 2001-2015; Nova Scotia-PEI, 2006-2015; Quebec, 2008-2014; Ontario-Manitoba, 2009-2015; British Columbia, 2012-2015. During the study period, the total number of ECTRs and rates per 100,000 exposures, respectively, were: United States, 40,258 and 65.7; United Kingdom, 343 and 232.6; Denmark, 616 and 305.5; Canada, 2709 and 177.5; case rates increased over time for the United States, Denmark, and Canada, but decreased in the United Kingdom. Across the United States and Denmark, HD was the preferred modality used. Toxins for which ECTR was most often used were: United States, ethylene glycol; Canada, methanol; United Kingdom, ethylene glycol; Denmark, salicylates. A high number of ECTRs were performed for atypical toxins such as acetaminophen and benzodiazepines. These data demonstrate a growing use of HD for poisoning with significant regional variations in the overall rates and indications.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".