A fatal case of Munchausen syndrome: Forensic dermatology aids in establishing the diagnosis
Bibliographic record
Abstract
Munchausen syndrome (factitious disorder imposed on self), a condition in which the patient intentionally injures themselves to create disease signs and symptoms, is difficult to diagnose. The affected individual not only seeks attention but also sympathy for their illness. A 31-year-old woman with cutaneous Munchausen syndrome who had persistent cutaneous ulcers and new abscesses is described; she died during her hospitalization and postmortem biopsies of her skin lesions and lungs both demonstrated polarizable foreign bodies. Fatal Munchausen syndrome has not commonly been described; including the woman in this report, we are aware of 19 decedents. The cause, mechanism, and manner of death have varied. Like our patient, the manner of death was most frequently undetermined since the circumstances did not permit accident to be differentiated from suicide. In conclusion, forensic dermatology aided in establishing the diagnosis of fatal cutaneous Munchausen syndrome in the woman we report. A biopsy of her non-healing ulcer showed polarizable foreign material that she had inoculated into her skin. Her condition involved not only self-inflicted skin abscesses, but also similar manifestations in her lungs. To prevent unnecessary laboratory tests and procedures and possibly death, healthcare providers need to consider the possibility of Munchausen syndrome.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".