Demodectic Mange, Dermatophilosis, and Other Parasitic and Bacterial Dermatologic Diseases in Free-Ranging White-tailed Deer ( <i>Odocoileus virginianus</i> ) in the United States From 1975 to 2012
Bibliographic record
Abstract
The white-tailed deer (Odocoileus virginianus) is a common and widespread North American game species. To evaluate the incidence, clinical manifestations, demography, and pathology of bacterial and parasitic dermatologic diseases in white-tailed deer in the southeastern United States, we retrospectively evaluated white-tailed deer cases submitted to the Southeastern Cooperative Wildlife Disease Study from 1975 to 2012. Among 2569 deer examined, bacterial or parasitic dermatologic disease was diagnosed in 88 (3.4%) individuals, with Demodex spp (n = 37; 42.0%) and Dermatophilus congolensis (n = 19; 21.6%) as the most common causes. Demodicosis was significantly more common in deer older than 2 years and was most often detected in the fall; no statistically significant sex predilection was identified. Affected animals had patchy to generalized alopecia, often distributed over the head, neck, limbs, and trunk; microscopic lesions included epidermal crusts and cutaneous nodules with mild perifollicular, lymphoplasmacytic inflammation. Dermatophilosis was most common in males younger than 1 year that were often found dead. Crusting, erythema, and alopecia occurred on the face, ears, and distal extremities. Less commonly, infectious dermatologic diseases were associated with other bacteria (n = 13; 14.8%), fungi (n = 5; 5.7%), ectoparasites (chiggers, lice, mites, and ticks; n = 11; 12.5%), and larval nematodes (n = 7; 8.0%). Population-level effects of these diseases in white-tailed deer are likely minimal; however, due to their dramatic presentation, demodicosis, dermatophilosis, and other infectious skin diseases can be of concern to hunters and, in some cases, may have zoonotic potential.
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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 | 0.001 |
| 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) | 0.001 | 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".