Trace Element Levels in Canine Tear Film
Bibliographic record
Abstract
OBJECTIVE: To evaluate the levels of trace elements in the tear film of healthy dogs. ANIMALS STUDIED: Twenty-five healthy Labrador retrievers. PROCEDURES: Tear samples were collected from the ventral conjunctival fornix of each dog using a Schirmer tear test strip. Elemental analysis was conducted using the particle-induced X-ray emission (PIXE) method with a 1.7 MV Pelletron accelerator. Trace element levels were compared across all dogs using the Kruskal-Wallis test with post hoc Tukey analysis and between male and female dogs using the Mann-Whitney test. RESULTS: The study included 14 neutered males and 11 spayed females, with a mean (±SD) age of 19.8 ± 3.1 months (range 14.3-26.5, median 18.8 months). Elemental analysis of tear samples revealed the presence of calcium (Ca), chloride (Cl), iron (Fe), phosphorus (P), potassium (K), sodium (Na), and sulfur (S). The median ± SEM peak area levels were highest for Cl (69 530 ± 1432) and K (5782 ± 514), followed by S (3545 ± 772), Na (3387 ± 159), P (1408 ± 160), Ca (593 ± 85), and Fe (276 ± 206). Compared to males, female dogs had significantly higher peak areas for S (2272 ± 549 vs. 4389 ± 1484, p = 0.037) and Ca (536 ± 76 vs. 800 ± 148, p = 0.029). CONCLUSIONS: This study provides a detailed assessment of the trace element composition in the tear film of healthy dogs. These findings enhance our understanding of canine ocular surface physiology and may have potential implications for the diagnosis and management of ocular surface diseases 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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".