Kinetics, dialysis systems, adequacy Postdialysis rebound in a case of acute methanol poisoning
Bibliographic record
Abstract
Introduction: Methanol poisoning can lead to complications that include metabolic acidosis, visual impairment and death. Treatment options include ethanol, fomepizole, and hemodialysis (HD). Objective: To report on the occurrence of post dialysis methanol rebound during treatment. Method and Findings: A 40‐year‐old male with a history of schizophrenia and suicide attempts presented to the emergency room after reportedly ingesting 1 quart of windshield washer fluid. The patient presented with a preliminary blood chemistry of methanol 390 mg/dL, ethanol 48 mg/dL, glucose 93 mg/dL, Na 138 meq/L, K 3.8 meq/L, Cl 98 mmol/L, CO2 26 mmol/L, urea 16 mg/dL, creatinine 1.2 mg/dL, and an anion gap of 14 mmol/L. The patient was started on 1360 mg of fomepizole (12:50 AM) followed by HD for 4 hours. A second dose of fomepizole (900 mg) was administered at 8:00 AM. In addition, another HD session was started at 12:00 PM and continued for 4 hours. A third dose of fomepizole (700 mg) was administered at 8:50 PM. Finally, a third HD session was started the next day at 3:05 PM and lasted 3 hours. Table 1 illustrates methanol levels in relation to each HD session. Findings: Methanol concentration after the first HD increased from 100 mg/dL to 127 mg/dL (27%) in 5 h 20 m. It also increased from 35 mg/dL to 50 mg/dL (43%) 14 h 45 m after the second HD. Conclusions: Close attention must be paid to the potential for post dialysis methanol rebound. It is recommended that methanol levels continue to be monitored for several hours after HD. Methanol levels before and after each hemodialysis Start HD #1 (2:40 AM) End HD #1 (6:40 AM) Start HD#2 (12:00 PM) End HD#2 (4:00 PM) Next Day (6:45 AM) Methanol (mg/dL) 324 100 127 35 50 Rebound 27% 43%
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".