Interpatient and Intrapatient Variability in Phenytoin Protein Binding
Bibliographic record
Abstract
The authors retrospectively assessed the relation between total and free serum concentrations and serum albumin in a sample of hospitalized patients to evaluate how well free serum concentrations can be estimated from total phenytoin serum concentrations. The authors also assessed the interpatient and intrapatient variability of the phenytoin free fraction. Paired serum samples of total and free phenytoin serum concentrations and serum albumin were obtained from 48 hospitalized patients (28 males, 20 females; mean age, 51 y; range, 13-90 y). Concomitant medications were recorded. Phenytoin free fraction and adjusted total phenytoin serum concentrations (adjusted for serum albumin) were calculated. One hundred sixty-three samples were obtained (mean, 3.4 samples per patient; range, 1-16 samples); 28 patients had more than one pair of samples obtained. Mean phenytoin free fraction was 15% +/- 7% (range, 4%-61%) for the 163 samples. The variability for the total, free, and free fractions were 65%, 75.9%, and 45.8%, respectively. There was significant variability in the phenytoin free fraction within individual patients who had more than one pair of serum concentrations obtained. The intraindividual coefficient of variation in phenytoin free fraction was 85% +/- 21.3% (range, 2%-94%). Despite strong overall correlation between the total phenytoin serum and free serum concentrations, there is excessive variability in phenytoin protein binding. Correction for serum albumin was not useful in this patient group. Because of significant interpatient and intrapatient variability in phenytoin serum concentrations, monitoring of total serum concentrations is unreliable and free phenytoin serum concentrations should be considered for monitoring in hospitalized patients.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".