Formic Acid and Methanol Concentrations in Death Investigations
Bibliographic record
Abstract
Methanol ingestion results in the formation of formic acid, a toxic metabolite that can cause metabolic acidosis. Methanol toxicity is therefore dependent on the amount of methanol ingested, the nature of treatment received, elapsed time since ingestion, and the accumulation of formic acid. Both methanol and formic acid concentrations are determined at this laboratory using headspace gas chromatography. An examination of 12 fatalities attributed to methanol poisoning is presented. Six individuals were found deceased, and their postmortem methanol and formic acid concentrations ranged from 84 to 543 mg/dL and 64 to 110 mg/dL, respectively. In the other six individuals, hospital treatment such as bicarbonate, ethanol infusion, and hemodialysis was administered. Antemortem methanol and formic acid concentrations ranged from 68 to 427 mg/dL and 37 to 91 mg/dL, respectively, whereas corresponding postmortem methanol and formic acid levels ranged from undetectable to 49 mg/dL and undetectable to 48 mg/dL, respectively. Hospital treatment of formic acid toxicity resulted in significantly reduced postmortem methanol and formic acid concentrations. Furthermore, the toxicological relevance of nine methanol-positive cases where postmortem methanol concentrations ranged from 3 to 142 mg/dL, with corresponding formic acid levels of less than 10 mg/dL, is discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".