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Record W2421068952 · doi:10.1093/milmed/167.11.939

Therapeutic Effects of Cooling Swine Skin Exposed to Sulfur Mustard

2002· article· en· W2421068952 on OpenAlexaff
Peggy Nelson, Ira Hill, John Conley, K L Blohm, Corey Davidson, Thomas W. Sawyer

Bibliographic record

VenueMilitary Medicine · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Exposure and Toxicity
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsSulfur mustardMedicineLesionSkin lesionToxicitySurgeryDermatologyPhysiologyToxicologyInternal medicineBiology

Abstract

fetched live from OpenAlex

Recent world events have highlighted the need for effective medical therapies for chemical weapon injuries. Of the chemical weapon agents, perhaps one of the most widely used, both historically and most recently in the Iran-Iraq War, is sulfur mustard (HD). No effective antidotes exist for this vesicant agent and, to this day, HD casualties are treated entirely symptomatically. Previous work carried out in this laboratory has indicated that cooling HD-exposed tissue may ameliorate the resultant injury. To further examine this, an anesthetized domestic swine model was used to investigate whether alteration of skin temperature had any effect either visually or histopathologically on the development and progression of HD-induced skin lesions over 7 days. Exposure of swine skin to HD vapor resulted in lesions whose severity was exposure time related (4, 8, 12, and 16 minutes). Postdecontamination heating of skin above ambient temperature (approximately 39 degrees C) resulted in worsening of the lesion, whereas postdecontamination cooling (approximately 15 degrees C) for between 2 to 4 hours postexposure lessened the severity of HD-induced injury. The authors conclude that the early, noninvasive and simplistic act of cooling HD-exposed skin may have a salutary effect on the severity of HD-induced cutaneous lesions.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.026
GPT teacher head0.225
Teacher spread0.199 · 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.

Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations16
Published2002
Admission routes1
Has abstractyes

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