Impact of methanol intoxication on the human electrocardiogram
Bibliographic record
Abstract
BACKGROUND: Methanol is a common commercial compound that can lead to significant morbidity and mortality with high levels of exposure. The purpose of this study was to describe electrocardiographic (ECG) changes associated with methanol intoxication. METHODS: A retrospective chart review was conducted with data from Kingston General Hospital collected between 2006 and 2011. Patient data, including demographics, medications, and laboratory data were recorded. Twelve-lead ECGs were obtained and changes were noted in relation to timing and extent of methanol intoxication. RESULTS: Nine patients with a mean age of 45 years were analyzed. All patients ingested methanol orally and presented to hospital between < 1 to 25 h after ingestion. The mean plasma methanol concentration on admission was 49.8 mmol/L. A lower pH and higher plasma methanol concentration were associated with multiple ECG changes. On admission, ECG changes included sinus tachycardia (44%), PR prolongation (11%), QTc prolongation (22%) and non-specific T-wave changes (66%). One patient developed a type-1 Brugada ECG pattern. During their course in hospital, 7 patients required dialysis, 3 required mechanical ventilation, 3 developed visual impairment, and 1 died. All ECG changes normalized while in hospital. CONCLUSIONS: Methanol intoxication can lead to several ECG changes with sinus tachycardia and non-specific T-wave changes being the most common. These changes were more prominent in cases of severe acidosis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".