L-Carnitine for Acute Valproic Acid Overdose: A Systematic Review of Published Cases
Bibliographic record
Abstract
OBJECTIVE: To review the evidence supporting the efficacy and safety of l-carnitine in the management of acute valproic acid overdose. DATA SOURCES: MEDLINE (1950-May 2010), EMBASE (1980-May 2010), and Google Scholar (to May 2010) were searched, using the terms carnitine, valproic acid, and carnitine for valproic acid overdose. Reference citations from identified publications were reviewed. STUDY SELECTION AND DATA EXTRACTION: Full-text publications evaluating the use of l-carnitine for management of valproic acid overdose in humans were sought. All studies, regardless of design, case series, and case reports reporting efficacy or safety endpoints were included. All languages were included. Two authors extracted primary data elements including patient demographics, presenting features, clinical management, and outcomes. DATA SYNTHESIS: Seven articles discussing 8 patients and 1 reporting safety data from records of 674 patients were reviewed. Reports covered both pediatric and adult patients with acute exposures to valproic acid mono- and polydrug overdose who were treated with various regimens of l-carnitine. All patients recovered clinically and no adverse effects were noted. CONCLUSIONS: Published evidence of the efficacy and safety of l-carnitine as an antidote for acute valproic acid overdose is limited. Based on the available evidence, it is reasonable to consider l-carnitine for patients with acute overdose of valproic acid who demonstrate decreased level of consciousness. We recommend intravenous administration of 100 mg/kg once, followed by infusions of 50 mg/kg (to a maximum of 3 g per dose) every 8 hours thereafter, continuing until ammonia levels are decreasing (if they were elevated initially) and the patient demonstrates signs of clinical improvement or until adverse events associated with l-carnitine occur.
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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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".