Successful treatment of carbamazepine poisoning with hemodialysis: A case report and review of the literature
Bibliographic record
Abstract
A 48-year-old man was brought to the emergency room after ingesting an unknown amount of carbamazepine. He was unconscious and not responding to the noxious stimuli. He was intubated and was placed on mechanical ventilation because of respiratory insufficiency. Primary detoxification was performed with a gastric lavage and charcoal instillation. His serum carbamazepine level was 25.6 mcg/mL at the time of admission. His computed tomography of the brain was normal. He was managed conservatively but there was no improvement in his neurological status in the next 24 hours. Serum carbamazepine level was repeated and reported to be 28.3 mcg/mL. As there was no improvement in his sensorium and the serum carbamazepine levels remained persistently high, extracorporeal removal of carbamazepine was attempted. As the facility to carry out hemoperfusion was not available immediately, the decision to initiate hemodialysis was taken. After 3 sessions of hemodialysis, his sensorium improved markedly and the carbamazepine level at this time was within the therapeutic range. He was discharged after psychiatry consultation and counseling. We review the literature regarding extra corporeal techniques for the removal of carbamazepine and discuss them in this article.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".