Tear production in canine neonates – evaluation using a modified Schirmer tear test
Bibliographic record
Abstract
PURPOSE: The ability of human newborns to produce tears has been a subject of controversy in the literature since the mid-20th century, and there has been considerable debate as to whether they are able to produce tears. Recently, it was established that total tear secretion (reflex + basal) in full-term infants is similar to those of adults whereas both reflex and basal tear production is reduced in premature babies. The objectives of this study were to assess whether newborn dogs have measurable aqueous tear production at the fourth week of life and to evaluate a modified Schirmer tear test (mSTT) as a useful method for measuring neonatal tear production in dogs. METHODS: Thirty four-week-old healthy puppies from six litters were evaluated. A control group was composed of 10 normal adult dogs. The mSTT strips were obtained by cutting a 5 mm-wide strip in half (making two 2.5 mm-wide strips). The mSTT1 was performed in puppies and adult dogs. Values were compared using t-tests. RESULTS: In neonates, the average value for the mSTT1 was 13.6 ± 3.07 (range = 7-19 mm/min), which was significantly lower in neonates than in adult dogs (23.25 ± 3.5, range = 17-30 mm/min, P < 0.0001). CONCLUSIONS: Canine neonates do produce tears by the fourth week of life, which can be successfully measured with the mSTT. This report established for the first time that canine neonates have significantly reduced total (reflex + basal) tear secretion compared to adults.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".