Bibliographic record
Abstract
OBJECTIVE: To report a case of phenytoin toxicity potentially associated with concurrent diazepam therapy. CASE SUMMARY: A 44-year-old First Nations man presented to the emergency department with headache, nystagmus, diplopia, and ataxia. Apart from a long-standing seizure disorder, his past medical history was unremarkable. The antiepileptic drug regimen of phenytoin, phenobarbital, and lamotrigine had been unchanged for almost 5 months. Total phenytoin serum concentration reported 2 weeks prior to hospital admission was 8 micro g/L. Two days prior to admission, he was prescribed amoxicillin and diazepam; he denied use of nonprescription or herbal medications. The serum phenytoin concentration drawn in the hospital was 37 micro g/mL. Both phenytoin and diazepam were stopped, and the symptoms resolved. His neurologic abnormalities were attributed to phenytoin toxicity caused by an interaction with diazepam. DISCUSSION: The literature documenting a potential interaction between diazepam and phenytoin is conflicting. Case reports and controlled studies have demonstrated both increases and decreases in serum phenytoin concentrations when these agents were administered concomitantly. Phenytoin induces the metabolism of drugs that are substrates of CYP2C, CYP2D, and CYP3A; however, phenytoin is eliminated predominantly by CYP2C9- and CYP2C19-dependent hepatic metabolism. Diazepam is one example of a drug that is extensively metabolized by CYP2C19 and could potentially influence phenytoin elimination by acting as an alternate substrate for this isoenzyme. In our patient, the timing of drug administration, clinical and physical examination findings, and laboratory data suggest that diazepam therapy resulted in phenytoin toxicity. Use of the Naranjo probability scale indicated a probable relationship between the adverse clinical effects observed and phenytoin and diazepam coadministration in this patient. CONCLUSIONS: Phenytoin is a known inducer of drugs metabolized by CYP2C, CYP2D, and CYP3A, but its own metabolism may be altered by drugs influencing CYP2C9 or CYP2C19, such as diazepam. Agents not acting as enzyme inhibitors or inducers, but instead behaving as alternate substrates for enzyme-binding sites, may produce clinically relevant drug interactions through an underrecognized mechanism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".