Case Reports of Extracorporeal Treatments in Poisoning: Historical Trends
Bibliographic record
Abstract
There are currently limited data on the trends in case reporting of poisoned patients undergoing enhanced elimination with an extracorporeal treatment (ECTR). The present manuscript specifically reviews the longitudinal trends of reports according to technique, poison, and country of publication. To identify case reports of ECTR use in the management of poisoning, multiple databases were searched. There were no limitations on language and year of publication. All case reports describing individual patients undergoing ECTR with the intent of enhancing the elimination of a poison were included in the analysis. Since 1913, 2908 reports were identified. There were an increasing number of published reports with time except for a slight decrease during the 1990s. Hemodialysis was by far the most commonly used ECTR in poisoning, followed by hemoperfusion. The number of reported peritoneal dialyses decreased steadily since 1980s. Methanol, ethylene glycol, lithium, and salicylates remained among the most commonly reported poisons in every decade. The large majority of publications originated from either Europe or North America, and more specifically from the United States, Germany, the United Kingdom, and China. Despite the emerging apparition of new techniques, hemodialysis remains to this day the favoured ECTR in the treatment of poisoned patients.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".