Excited Delirium Syndrome (ExDS): Understanding the Issues and Reducing the Risks Associated with Police Use of Force
Bibliographic record
Abstract
The review of sudden and unexpected in-custody deaths (I-CDs) clearly demonstrates that there is a cluster of features which indicate that a subject is suffering from a medical emergency.For those who most frequently deal with these subjects (e.g., law enforcement, paramedics, emergency physicians, medical examiners) this is a real issue with serious implications.The labelling of this cluster of features as Excited Delirium Syndrome (ExDS) continues to be contentious.However a standardized and concise label with which meaning (e.g., medical emergency) can be assigned is necessary for the recognition, identification, intervention and treatment of these subjects.Additionally, despite there being many risk factors and a multitude of etiologies and pathophysiologies for ExDS, there are prevention and intervention strategies that can be employed within these dynamic and rapidly unfolding events to diminish adverse outcomes.Through the theoretical lens of symbolic interactionism and the sociology of diagnosis, this research examines the meaning attributed to ExDS, how this meaning influences actions as well as the risks and benefits of medicalization.This research presents promising intervention strategies for gaining control of these subjects, as well as risk factors and officer safety concerns.Furthermore, through the use of grounded theory and excerpts from use of force reports, this research provides an interpretive account of the extreme and violent nature of encounters with probable cases of ExDS, providing a better understanding of these situations.This research represents a new area of inquiry into non-fatal cases of ExDS and the prevention of sudden and unexpected I-CDs.The use of a mixed methods research iii design utilizes the strengths of qualitative and quantitative methods to analyze police use of force reports.This provides the opportunity to triangulate the results from each of these differing methodological approaches in order to elucidate, validate and generalize the findings.This gives both the depth and breadth required to inform law enforcement training and policy in the area of use of force and medically-high risk situations.As such, this research provides grounded recommendations for policy and training as a delivery mechanism of meaning as well as for equipment and use of force reporting.Thus, the overall focus and intent of this research is reducing the risk of I-CDs and improving police and public 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".