Endogenous carbon monoxide due to hemolytic anemia: A forensic red herring
Bibliographic record
Abstract
Carbon monoxide (CO) toxicity associated with exposure to an environmental, exogenous source, is routinely investigated in the field of forensics. Paramedics responded to the home of a 60-year-old woman who complained of persistent nausea, dizziness, and fatigue. Her initial carboxyhemoglobin (COHb) saturation was 25% as measured by paramedics in the field via pulse CO-oximetry (SpCO) and was, 2 hours later, confirmed by hospital laboratory spectrophotometric analysis to be 16% after initial treatment in the emergency department. The clinical presentation of environmental CO exposure and subsequent death notification to the North Carolina Office of the Chief Medical Examiner prompted an extensive investigation into the suspected residential source of CO, which ultimately ruled out all exogenous sources. The medicolegal death investigator later discovered an updated hematology consultation note, which determined the actual source of the CO to be endogenously produced from disease. Herein, we report an unusual fatality involving enhanced endogenous CO production caused by warm autoimmune hemolytic anemia. This unique case report and brief literature review of disease-related elevation of endogenous CO will shed light on this lesser-known phenomenon alerting the forensic community to its potential occurrence and need for consideration when sources of environmental exposure have been exhausted.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".