Extracorporeal treatments in poisonings from four non‐traditionally dialysed toxins (acetaminophen, digoxin, opioids and tricyclic antidepressants): A combined single‐centre and national study
Bibliographic record
Abstract
The use of extracorporeal treatments (ECTRs) for poisonings with four non-traditionally dialysed toxins (NTDTs) is increasing in the United States. This study evaluated whether ECTRs are prescribed for toxin removal or the treatment of other medical illnesses or complications. We performed a 2-Phase retrospective analysis evaluating the main indication for ECTRs in patients with poisoning from a NTDT (defined for this study as acetaminophen, opioids, tricyclic antidepressants (TCAs) or digoxin) and ECTR. The first phase assessed all cases from a single site (New York City Poison Control Center) between the years 2000 and 2016, and the second phase surveyed all United States Poison Control Centers (PCCs). In Phase 1, demographics, toxin ingested and main indication for ECTR were extracted. In Phase 2, a query to the National Poison Data System using the a pragmatic subset of inclusion criteria from Phase 1 restricted to single toxin ingestions over a narrower time frame (2014-2016) provided the cases for study. A structured online questionnaire was sent to all United States PCCs to request their database review regarding the indication for ECTR for their cases. In Phase 1, 92 cases met inclusion criteria. In Phase 2, 519 cases were screened and 425 met inclusion criteria. In Phase 1 91/92 (98.9%) and Phase 2 411/425 (96.7%), of extracorporeal treatments were used to treat underlying medical conditions or poisoning-related complications rather than accelerate toxin removal. The increasing number of ECTRs reported in patients who ingested one of the four NTDTs thus appears to be for medical indications rather than attempts at toxin removal, a distinction that is important.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".