Bibliographic record
Abstract
Poisoning occurs after exposure to any of a number of substances, including medicines, which can result in severe toxicity including death. The nephrologist may be involved in poisonings that cause kidney disease and for targeted treatments. The overall approach to the poisoned patient involves the initial acute resuscitation and performing a risk assessment, whereby the exposure is considered in terms of the anticipated severity and in the context of the patient's status and treatments that may be required. Time-critical interventions such as gastrointestinal decontamination ( e.g. , activated charcoal) and antidotes are administered when indicated. The nephrologist is usually involved when elimination enhancement techniques are required, such as urine alkalinization or extracorporeal treatments. There is increasing data to guide decision making for the use of extracorporeal treatments in the poisoned patient. Principles to consider are clinical indications such as whether severe toxicity is present, anticipated, and/or will persist and whether the poison will be significantly removed by the extracorporeal treatment. Extracorporeal clearance is maximized for low-molecular weight drugs that are water soluble with minimal protein binding (<80%) and low endogenous clearance and volume of distribution. The dosage of some antidotes ( e.g. , N-acetylcysteine, ethanol, fomepizole) should be increased to maintain therapeutic concentrations once the extracorporeal treatment is initiated. To maximize the effect of an extracorporeal treatment, blood and effluent flows should be optimized, the filter with the largest surface area selected, and duration tailored to remove enough poison to reduce toxicity. Intermittent hemodialysis is recommended in most cases when an extracorporeal treatment is required because it is the most efficient, and continuous kidney replacement therapy is prescribed in some circumstances, particularly if intermittent hemodialysis is not readily available.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".