Phenytoin overdose treated with hemodialysis using a high cut‐off dialyzer
Bibliographic record
Abstract
Abstract We describe the case of a 52‐year‐old man who presented after having ingested an unknown quantity of phenytoin. Peak phenytoin concentration was 51.2 mg/L (therapeutic range 10–20 mg/L). Five days after admission, the patient became comatose and was intubated. Because of persistent toxic phenytoin levels and unchanged clinical status for 12 days, hemodialysis (HD) was prescribed to enhance elimination of phenytoin. HD was performed using a Gambro TheraliteTM filter (Baxter International Inc., Deerfield, USA), a high cut‐off filter that allows the removal of molecules of up to 45 kDa. Phenytoin concentration readily decreased during the 8‐hour HD treatment from 38.9 mg/L to 27.8 mg/L (28.5% decrease); during HD, phenytoin half‐life was 18.5h (compared to 1109.8h before HD and 56.3h after HD), phentyoin clearance averaged 80.1 mL/min and a total of 1.1 g of phenytoin was removed. Albumin removal from the Theralite filter was most important at the beginning of HD. The high clearance of phenytoin obtained with this filter was likely due to its high surface area rather than its capacity to remove the albumin‐phenytoin complex.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".