Efficacy of ex vivo decontamination methods for chemical warfare agents on military working dog (Canis familiaris) cadaver skin tissue
Bibliographic record
Abstract
OBJECTIVE: Evidence-based evaluation of working dog decontamination is needed following several contemporary events involving threats for contamination with hazardous materials. The purpose of this study was to describe the behavior of ex vivo neat chemical warfare agent exposure on military working dog breed-specific canine cadaver tissue and measure the potential effectiveness of standard and potential alternative decontamination methods. METHODS: Previously frozen German Shepherd, Belgian Malinois, and Labrador Retriever full-thickness skin tissue with attached hair coat was used to test the efficacy of decontamination procedures in the removal of sulfur mustard blister agent (HD) and organophosphate nerve agent (VX) chemical warfare contaminants. Four different decontamination treatments were evaluated: none, microfiber towel only (MFTO), low-water method (LWM), and high-water method (HWM). The lesser/nonhaired inner ear, paw pads, and underbelly were evaluated using a Reactive Skin Decontamination Lotion treatment. RESULTS: The MFTO condition showed a significant removal amount of HD and VX agent from hair coats. An average of 83.1 percent HD and 80.9 percent VX reduction in the initially applied agent was observed with microfiber towel wipes in all tested breeds. As tested, the MFTO method resulted in less recovered agent than the 4 percent chlorhexidine scrub LWM. The HWM resulted in an average of 80.3 percent HD and 98.7 percent VX reduction in the initially applied agent. CONCLUSION: The data suggest that the MFTO method alone may be an effective field expedient decontamination method for VX and HD in situations with limited water resources.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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 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".