Adverse outcomes in non-fatal use of force encounters involving excited delirium syndrome
Bibliographic record
Abstract
This study examined the risk of adverse outcomes during non-fatal encounters with subjects exhibiting features of Excited Delirium Syndrome (ExDS). Data for the study was collected over a five-year period through standardized reporting in a large Canadian law enforcement agency. Consistent with previous research, the presence of six or more of the ten features of ExDS was used to identify a probable case. Force was applied on 10,718 subjects, 197 (1.8%) of which were probable ExDS. Logistic regression were used to model the odds that use of force (UoF) interventions used on subjects in a state of probable ExDS resulted in adverse outcomes. Probable ExDS was one of the most important predictors of adverse outcomes in UoF encounters, even after controlling for associated risk factors. There were significantly higher odds that UoF was ineffective on subjects exhibiting more features of ExDS, resulting in an increased amount of force applied. In contrast, there were significantly lower odds of injury from UoF for individuals exhibiting probable ExDS. Officers, however, were at a higher risk of injury when dealing with those displaying a greater number of features. These results underscore the risks inherent to incidents involving cases of probable ExDS. A greater understanding of such risks may improve response strategies and promote public and police safety.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".