Tear production as measured by Schirmer tear test-1 in dogs with atopic dermatitis.
Bibliographic record
Abstract
Objective: The objective was to measure tear production using Schirmer tear test-1 (STT-1) in dogs with atopic dermatitis, to evaluate for abnormal tear production. Animals: We evaluated 47 client-owned dogs diagnosed with atopic dermatitis based on history, clinical signs, completion of an elimination diet trial, and fulfillment of at least 5 of Favrot's diagnostic criteria. Procedure: Schirmer tear test-1 was conducted on each dog. Values < 15 mm/min were consistent with low tear production and suggestive of keratoconjunctivitis sicca. Values > 25 mm/min were consistent with epiphora due to excess lacrimation. Results: Twenty-one of 47 atopic dogs had STT-1 values outside the normal range in at least 1 eye. Three had STT-1 values < 15 mm/min and 18 had STT-1 values > 25 mm/min in at least 1 eye. Conclusion and clinical relevance: These findings suggest atopic dermatitis may be associated with altered tear production. However, the absence of a complete ophthalmic assessment precludes definitive conclusions regarding keratoconjunctivitis sicca and epiphora. Future studies to validate these observations could help determine whether routine screening of atopic dogs using the STT-1 can aid in detecting ocular manifestations of canine atopic dermatitis to optimize animal care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".