Measurement of tear production and intraocular pressure in conscious captive European fallow deer (<i>DAMA dama</i>)
Bibliographic record
Abstract
Abstract Normal values for intraocular pressure (IOP) and tear production in conscious cervids have not been reported to date. Based on trends in zoological institutions to perform non‐anaesthetized health exams, it is applicable to establish normal values in conscious animals, as anaesthesia and sedation can alter these parameters. The goal of this study was to estimate intraocular pressures using rebound tonometry and measure tear production values in a group of healthy, conscious, European fallow deer utilizing chute restraint. Evaluation of these values with regards to instrumentation and restraint variables will be assessed. Complete ophthalmic examinations, including estimation of IOP with rebound tonometry and measurement of tear production with Schirmer tear tests (STT) were performed on nine conscious European fallow deer (Dama dama) restrained in a chute. Correlations between IOP on the unspecified (P) and the equine (H) settings, as well as IOP and STT differences between left (OS) and right (OD) eyes were evaluated, in addition to assessment of correlations between right and left lateral recumbency on IOP and STT. Tear production measurements were 18.7 ± 5.1 mm min−1 with a 95% confidence interval (CI) range of 16.4–21.1 mm min−1. Intraocular pressure measurements for the P setting were 16.1 ± 4.5 mmHg with a 95% CI range of 14.1–18.2 mmHg, and for the H setting were 21.5 ± 5.1 mmHg with a 95% CI range of 19.1–23.9 mmHg. No statistically significant difference (P > 0.05) was found between OS and OD in any test. Neither left nor right lateral recumbency was found to have a statistically significant effect on IOP or STT. This study represents the first assessment of ophthalmic parameters in conscious fallow deer with rebound tonometry and STT.
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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 | 0.001 |
| 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".