Management of the effects of exposure to tear gas
Bibliographic record
Abstract
#### Summary points Despite the frequent use of riot control agents by European law enforcement agencies, limited information exists on this subject in the medical literature. The effects of these agents are typically limited to minor and transient cutaneous inflammation, but serious complications and even deaths have been reported. During the 1999 World Trade Organisation meeting and at the 2001 Summit of the Americas in Quebec, exposure to tear gas was the most common reason for medical consultations.1 2 Primary and emergency care physicians play a role in the first line management of patients as well as in the identification of those at risk of complications from exposure to riot control agents. In 1997 the National Poisons Information Service in England received 597 inquiries from doctors seeking advice about problems related to crowd control.3 Our article reviews the different riot control agents, including the most common tear gases and pepper sprays, and provides an up to date overview of related medical sequelae. We searched the following resources for relevant information on the medical toxicity and management of acute exposure to tear gas and pepper spray: Medline, PreMedline, Embase, CINAHL, SCIRUS, the Cochrane Library, ISI Web of Knowledge, Toxnet, Google Scholar, and personal archives. We used the subject headings “riot control agents”, “pepper spray”, “lacrimator”, “tear gas”, “irritants”, “incapacitating agents”, as well as the toxicological terms “chlorobenzylidene-malononitrile”, “chloroacetophenone”, “dibenzoxazepine”, “chlorodiphenylarsine” …
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".