Gut decontamination in the poisoned patient
Bibliographic record
Abstract
Poisoning management includes gastrointestinal decontamination strategies to decrease the burden of poison entering the body and change the expected severe toxicity expected to a less toxic, more favourable outcome. Common modalities are orogastric lavage, oral-activated charcoal and whole-bowel irrigation. Endoscopic retrieval and laparotomy are rare options reserved for severe ingestions and body packers. Although supporting data are generally of low quality, gastrointestinal decontamination is likely to improve patient outcome in many situations. Unfortunately, technical limitations and contraindications can explain their infrequent use. Orogastric lavage can be useful for early lethal ingestions, albeit with significant complications such as aspiration and perforation. Activated charcoal cannot adsorb every substance. Usual dosing is 1 g/kg per dose. Whole-bowel irrigation is reserved for charged molecules or substances not adsorbed to activated charcoal but requires intact gut motility. Indications depend on several factors inherent to the ingestion (dose, time, poison) and patient's characteristics. During recent decades, studies of newer pharmaceuticals or modified-release formulations showed that significant amounts of poison, especially pharmacobezoars, persist in the gut hours postingestion, thus are amenable to gastrointestinal decontamination. Improved understanding of gut motility in volunteer studies and overdose showed clinically significant reduction in drug exposure with activated charcoal. The 1-h dogma for gastrointestinal decontamination, especially activated charcoal, is now obsolete. Clinicians must perform a risk assessment for each ingestion to determine the expected benefit at the time of decision-making, choosing the modality to achieve reduction in the toxicity burden while planning for complications or contraindications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".