Bibliographic record
Abstract
Self-inflicted gunshot wounds are a common modality of suicide. Cases with multiple gunshot wounds are rare. Problems with determining manner may arise when there is a lack of understanding of how and why they occur. Demographic data has seldom been explored in these cases. Gunshot wound suicides from the years 2015 to 2023 were reviewed at the Hillsborough County Medical Examiner's Office. Nineteen cases with multiple gunshot wounds were found. The multi-GSW cases, compared to the single-GSW cases, had a significantly increased proportion of revolvers (57.9% vs. 29.8%, P =0.008), lower muzzle energy handguns (86.7% vs. 39.6%, P <0.001), and shots to the torso (70.6% vs. 9.0%, P <0.001). Multi-GSW case decedents were significantly older than the single-GSW suicide average (16.2 y, P <0.001). There were also significant age differences between decedents who used the following: (Revolvers vs. semi-automatic pistols, 16.2 y, P <0.001; muzzle energy <400 vs. >400 J, 15.8 y, P <0.001; and shots to the torso vs. head, 8.4 y, P =0.002). In our population, older age was associated with factors that might necessitate multiple gunshot wounds in a suicide.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".