Evaluation of results for Schirmer tear tests conducted with and without application of a topical anesthetic in clinically normal dogs of 5 breeds
Bibliographic record
Abstract
OBJECTIVE: To evaluate, for clinically normal dogs, results of Schirmer tear tests in eyes without topical anesthetic (STT) and to detect differences associated with breed, sex, age, day, and time of day in eyes in which STT was performed after use of topical anesthetic (STTa). ANIMALS: 41 Beagles, 43 Labrador Retrievers, 25 Golden Retrievers, 26 English Springer Spaniels, and 22 Shetland Sheepdogs. PROCEDURE: Beagles had STT and STTa values measured twice daily for 5 days. Client-owned dogs of 4 other breeds had STT and STTa values measured once. RESULTS: Mean +/- SD values of Beagles for STT and STTa were 20.2 +/- 2.5 and 3.8 +/- 2.7 mm/min. Mean values for STT and STTa were as follows: Labrador Retriever, 22.9 +/- 4.1 and 9.6 +/- 3.8 mm/min; English Springer Spaniel; 20.7 +/- 3.2 and 5.4 +/- 3.4 mm/min; Golden Retriever, 21.8 + 3.7 and 8.8 +/- 3.1 mm/min; and Shetland Sheepdog, 15.8 +/- 1.8 and 3.6 +/- 2.8 mm/min. Overall mean values for STT and STTa were 20.2 +/- 3.0 and 6.2 +/- 3.1 mm/min. Differences for STT and STTa were detected among breeds, but significant differences were not associated with sex or age within each breed or in overall values for all dogs. CONCLUSIONS AND CLINICAL RELEVANCE: Results for the STT reported here compare favorably with reported values, except for results of Shetland Sheepdogs; however, results for the STTa differ dramatically from reported values. Clinicians should consider effects attributable to breed when evaluating results of STT and STTa in dogs.
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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 | 0.001 | 0.004 |
| 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) | 0.001 | 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".