Frequency and Etiology of Hand and Forearm Dermatoses Among Veterinarians
Bibliographic record
Abstract
Background: Veterinarians are exposed to a range of skin irritants and allergens, yet few studies have addressed the occurrence of dermatoses among veterinarians. Objectives: The goals of this study were to determine the frequency of noninfectious hand and forearm dermatoses among Kansas veterinarians, to estimate the role of occupational exposures in the aggravation of such dermatoses, to determine the frequency and nature of infectious dermatoses among veterinarians, and to investigate patterns of glove use. The secondary goals of this study were to collect information about the impact of skin disease on the lives and careers of veterinarians and to provide physicians with a practical approach to the treatment of veterinarians with dermatoses. Methods: A questionnaire was mailed to all members of the Kansas Veterinary Medical Association. Results: The response rate was 60%. Twenty-four of respondents reported noninfectious, recurrent/persistent hand or forearm dermatoses; 66% were work related. Large animal veterinarians (P= .026) and atopics (P= .009) were more likely than their counterparts to attribute their dermatoses to work-related factors. Thirty-eight percent of respondents had contracted at least one infectious skin disease from an animal. Veterinarians who never or rarely use gloves during obstetric procedures were more likely to report work-related dermatoses (odds ratio, 4.25; 1.78 < OR < 10.07;P< .001) than those who use gloves. Conclusion: Veterinarians are affected frequently by infectious and noninfectious dermatoses. Improvement of barrier protection habits during obstetric procedures would likely reduce the frequency of occupational dermatoses among veterinarians.
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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 | 0.002 |
| 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) | 0.004 | 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".