Textile and Shoe Allergic Contact Dermatitis in Military Personnel
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Allergic contact dermatitis is a common dermatosis among military personnel. Given the unique military demands, it is not surprising that shoe dermatitis and textile dermatitis are common. Our study aimed to compare the clinical and demographic parameters between military personnel and civilians evaluated for the suspicion of shoe and textile dermatitis in a tertiary clinic in Israel. METHODS: This retrospective cross-sectional study included 295 patients who were referred to a tertiary clinic for evaluation because of suspected shoe or textile dermatitis. Eighty-eight of the patients were soldiers. The patch tests were tailored according to the clinical presentation and relevant exposures. RESULTS: The 2 populations differed in several parameters. The duration of the dermatitis was longer in the civilian group. The atopy rate was significantly higher among military conscripts. The patch test reactivity and multiple patch test reactivity were lower in the army group. Dermatitis seen in the military group tended to be more widely distributed. CONCLUSIONS: Distinctive demographic and exposure patterns explain the differences observed between the 2 study groups. It is not surprising that irritant dermatitis is more common among military personnel, given the extreme military demands and higher atopy rate among soldiers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".