An Introduction to the Work of a Medical Examiner
Bibliographic record
Abstract
Medical examiners play an increasingly important role in society as unexpected and violent deaths increase, not only due to crime, but also due to new toxins in the environment, emerging diseases crossing from animals to humans, bizarre suicides, sadistic sexual practices, and other non-natural causes. John Miletich and Tia Lindstrom take us into the world of these medical detectives. Biological clues from bite marks and skin abnormalities to blood chemical levels and brain oxygenation are just some factors exposed in their quest for truth and justice. We learn the basics of death determination from rigor and livor mortis to signs of death by design, drug use, disease, suicide, and more. We also come to understand the tools of this work, from the Stryker's Saw to the grocer's scale, and tests that reveal factors from DNA evidence to toxins from insect bites. Each case begins with a biological mystery and ends with a conclusion that can provide loved ones with relief, or shock. Miletich, who trained at the Alberta Office of the Chief Medical Examiner and with the Royal Canadian Mounted Police, teams with Lindstrom to introduce readers to the medical examiner's role, including autopsy techniques and analysis. Twists and turns emerge as what was initially thought to be a murder proves to be suicide; what was suspected to be a natural death proves to be murder or environmental poisoning; or what was thought to be an accidental death proves to be something more sinister. This work includes appendices with guides to Medical Examiner organizations, seminars, and conventions.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.062 | 0.054 |
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".