Haemodialysis for hyperammonaemic encephalopathy
Bibliographic record
Abstract
Sir, We recently reported the usefulness of haemodialysis for treating hyperammonaemic coma complicating urinary diversions [1]. In our previous report, the obtained blood‐side ammonia clearances during high‐efficiency haemodialysis were similar to those found for urea (with respective values of 261.4±11.4 vs 262.6±47.2 ml/min) and comparable to those reported in vitro by Cordoba et al. [2]. The purpose of the present communication is to confirm the usefulness of haemodialysis for hyperammonaemic encephalopathy and to verify measured clearances. A 59‐year‐old female was brought to the emergency room with decreased alertness over a few few hours. She had known congenital uropathy which had required a right nephrectomy and an ureterosigmoidostomy. History was negative except for a deliberate discontinuation of laxatives the week before. Initial biochemistry serum results were as follows: creatinine 89 μmol/l, urea 10.3 mmol/l, sodium 147 mmol/l, chloride 119 mmol/l, bicarbonates 14.5 mmol/l. Arterial pH was 7.32 with a pCO2 of 28.5 mmHg. A bicarbonate infusion was initiated and netilmycine was empirically given intravenously. Because of neurological deterioration, she was intubated and transferred to the ICU. Serum ammonia concentration drawn upon admission came back markedly elevated at 228 μmol/l and increased to 379 μmol/l 5 h later when the patient deteriorated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".