Bibliographic record
Abstract
It is written in almost all articles on autoerotic deaths that these fatalities account for about 500 to 1000 deaths per year in the United States and Canada. However, contrary to the general belief, this incidence rate was not obtained through a study, but it is an estimation based on 30-year unpublished data from Canada and England. In the present retrospective study from 1985 to 2009, 38 cases of autoerotic deaths were identified in the province of Alberta (Canada). This number corresponds to an incidence of 0.56 autoerotic deaths per million inhabitants per year. The vast majority of these deaths are related to typical, predominantly asphyxial methods, such as hanging. The bodies were most commonly found in basements, bedrooms, and bathrooms. There is no clear evidence of a preferential time of day for these deaths, but there appear to be slightly more autoerotic deaths during summer. The incidence of autoerotic deaths is higher in big cities compared with rural areas. The previously published estimate of 500 to 1000 'autoerotic deaths per year should not be used for Canada. An incidence of 0.2 to 0.5 cases per million inhabitants per year is a better estimate of the incidence of autoerotic deaths. The incidence in United States should be reassessed using epidemiological studies.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".