Do Centenarians Die Healthy? An Autopsy Study
Bibliographic record
Abstract
BACKGROUND: Our goal was to assess the prevalence of common causes of death and the demographic variables in a selected population of centenarians. METHODS: The autopsy reports and medical histories of all individuals > or =100 years, dying unexpectedly out of hospital, were gathered from 42,398 consecutive autopsies, performed over a period of 18 years at the Institute of Forensic Medicine, Vienna. These records were evaluated with regard to age and sex, circumstances of death, season, time and the cause of death, as well as the presence of any other comorbidity. RESULTS: Forty centenarians (11 men, 29 women) were identified with a median age of 102 +/- 2.0 (range: 100-108) years. Sixty percent were described as having been healthy before death. However, an acute organic failure causing death was found in 100%, including cardiovascular diseases in 68%, respiratory illnesses in 25%, gastrointestinal disorders in 5%, and cerebrovascular disease in 2%. Additionally, centenarians suffered from several comorbidities (cardiac antecedents, neurologic disorders, liver diseases, cholecystolithiasis), which were not judged to be the cause of death. CONCLUSIONS: Centenarians, though perceived to have been healthy just prior to death, succumbed to diseases in 100% of the cases examined. They did not die merely "of old age." The 100% post mortem diagnosis of death as a result of acute organic failure justifies autopsy as a legal requirement for this clinically difficult age group.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".