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Record W4413969513 · doi:10.1177/17103568251367715

Weapons of War and Dermatology: A Comprehensive Review of Cutaneous Manifestations From Chemical Warfare Agents, Part II: Nerve Agents, Cyanides, and Riot Control Agents

2025· review· en· W4413969513 on OpenAlexvenueno aff
William J. Nahm, Emily C. Milam, David E. Cohen

Bibliographic record

VenueDermatitis · 2025
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Exposure and Toxicity
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNerve agentChemical Warfare AgentsDermatologyChemical warfareEngineeringBiochemical engineeringLawPolitical scienceOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

This second installment in a two-part comprehensive review explores chemical warfare agents that primarily cause systemic toxicity with secondary cutaneous manifestations: nerve agents, cyanides, and riot control agents (RCAs). While these agents are primarily known for their systemic effects, their dermatological manifestations can provide critical diagnostic clues in exposure scenarios. Part II examines the G-series, V-series, GV-series, and Novichok nerve agents; various cyanide compounds; and both historical and modern RCAs. For each agent class, the review details their historical contexts, physicochemical properties, mechanisms of action, dermatological manifestations, and current treatment approaches. The widespread use of RCAs in civilian law enforcement underscores the practical relevance of understanding these compounds. Additionally, despite international prohibitions, some of these agents continue to pose threats in modern conflicts and targeted assassinations. The review highlights the crucial role dermatologists can play in multidisciplinary response teams, as recognizing characteristic cutaneous changes may facilitate rapid diagnosis and life-saving intervention. Future preparedness efforts should incorporate specialized training for health care providers, emphasizing the distinct dermatological presentations of these agents.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.855
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.280
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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