Underlying medical conditions in cats with presumptive psychogenic alopecia
Bibliographic record
Abstract
OBJECTIVE: To identify underlying medical conditions in cats with a presumptive diagnosis of psychogenic alopecia. DESIGN: Case series. ANIMALS: 21 adult cats referred with a presumptive diagnosis of psychogenic alopecia. PROCEDURES: A detailed behavior and dermatologic questionnaire was completed by the primary caregiver, and complete behavioral and dermatologic examinations were performed. A standard diagnostic testing protocol that included cytologic examination of skin scrapings, fungal culture of hairs, evaluation of responses to parasiticides and an exclusion diet, assessment for atopy and endocrinopathies, and histologic examination of skin biopsy specimens was used to establish a definitive diagnosis in all cats. Cats that did not respond to an elimination diet were treated with methylprednisolone acetate to determine whether pruritus was a factor. RESULTS: Medical causes of pruritus were identified in 16 (76%) cats. Only 2 (10%) cats were found to have only psychogenic alopecia, and an additional 3 (14%) cats had a combination of psychogenic alopecia and a medical cause of pruritus. An adverse food reaction was diagnosed in 12 (57%) cats and was suspected in an additional 2. All cats with histologic evidence of inflammation in skin biopsy specimens were determined to have a medical condition, but of 6 cats without histologic abnormalities, 4 had an adverse food reaction, atopy, or a combination of the 2, and only 2 had psychogenic alopecia. CONCLUSIONS AND CLINICAL RELEVANCE: Results suggest that psychogenic alopecia is overdiagnosed in cats. Thorough diagnostic testing should be done before ascribing a behavioral cause to hair loss in cats.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".