An 8-Year Retrospective Study on Suicides in Washington, DC
Bibliographic record
Abstract
The suicide rate in the United States has been increasing steadily over the previous 10 years. In DC, these results are not mirrored. The suicide rate has a tendency to be lower than the rest of the country. During this retrospective review of suicides in DC, factors such as medical history and toxicology results were examined.In this study performed over 8 years (2009-2016), 394 suicides occurred. It was found that decedents committed suicide mostly by hanging (31.2%), firearms (20.3%), or drug intoxication (15.7%). The average age was 44.5 years. Similar to national statistics, male individuals committed suicide at a higher rate (77.9%) than did female individuals (22.1%). The toxicology data showed that ethanol (26.4%), antidepressants (20.1%), opioids (14.9%), and benzodiazepines (12.9%) were the drugs most frequently involved, although the finding of no drugs was most common (33.7%). Ethanol was present in 5 methods of suicide that include death by hanging, drowning, firearm, suffocation, and poisoning.This research provides information that may be useful for public health officials when confronting the issue of suicide. It is hoped that it will encourage other medical examiner offices to perform toxicological analysis and autopsy of all suicide cases.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".