Bibliographic record
Abstract
Sometimes mistakenly characterized as a 'universal antidote,' activated charcoal (AC) is the most frequently employed method of gastrointestinal decontamination in the developed world. Typically administered as a single dose (SDAC), its tremendous surface area permits the binding of many drugs and toxins in the gastrointestinal lumen, reducing their systemic absorption. Like other decontamination procedures, the utility of SDAC attenuates with time, and, although generally safe, it is not free of risk. A large body of evidence demonstrates that SDAC can reduce the absorption of drugs and xenobiotics but most such studies involve volunteers and have little generalizability to clinical practice. Few rigorous clinical trials of SDAC have been conducted, and none validate or refute its utility in those patients who are intuitively most likely to benefit. Over the past decade, a growing body of observational data have demonstrated that SDAC can elicit substantial reductions in drug absorption in acutely poisoned patients. The challenge for clinicians rests in differentiating those patients most likely to benefit from SDAC from those in whom meaningful improvement is doubtful. This is often a difficult determination not well suited to an algorithmic approach. The present narrative review summarizes the data supporting the benefits and harms of SDAC, and offers pragmatic suggestions for clinical practice.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".