Management of the effects of exposure to tear gas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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” …
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it