Intentional use of ertapenem to reduce valproic acid concentrations in overdose: two case reports
Bibliographic record
Abstract
Introduction Valproic acid (VPA) is used for the treatment of seizures and other conditions. Overdose present as central nervous system and respiratory depression, metabolic acidosis, and hyperammonemia. Management is mainly supportive, including activated charcoal, L-carnitine, and hemodialysis. Multiple reports have demonstrated reductions in therapeutic VPA concentrations with carbapenems.Case report 1 A 23-year-old male with bipolar disorder ingested 40 g of VPA. The initial VPA and ammonia concentrations were 300 µg/L and 192 µg/dL, respectively. The peak VPA and ammonia concentrations reached 894 µg/L and 740 µg/dL, respectively. He received multi-dose activated charcoal, L-carnitine, and ertapenem, followed by continuous renal replacement therapy (CRRT). His concentrations decreased rapidly, and he was extubated.Case report 2 A 33-year-old male ingested 10 g of VPA, multiple psychotropics, and alcohol. He had a GCS of 10, hypoxemia, and initial VPA and ammonia of 45 µg/L and 139 µg/dL, respectively. The peak VPA and ammonia were 317 µg/L and 255 µg/L, respectively. He received L-carnitine and ertapenem, with a subsequent decline in concentrations.Discussion Carbapenems may reduce VPA concentrations by 50%–80%. Our cases demonstrate the successful use of ertapenem in the management of severe VPA toxicity. Further studies may establish criteria for the use of carbapenems for treating VPA poisoning and whether they can reduce the need for hemodialysis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.011 | 0.007 |
| 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".