Development of reference intervals for pupillometry in healthy dogs
Bibliographic record
Abstract
Background: Pupillometry, the measurement of pupil size and reactivity to a stimulus, has various uses in both human and veterinary medicine. These reflect autonomic tone, with the potential to assess nociception and emotion. Infrared pupillometry reduces inaccuracies that may occur when the pupillary light reflex is determined subjectively by the examiner. To our knowledge, there are no published studies outlining normal reference intervals for automated pupillometry in dogs. Objective: automated pupillometry reference intervals from 126 healthy canine eyes. Methods: The pupillary light reflex (PLR) was measured with a handheld pupillometer (NeurOptics™ PLR-200™ Pupillometer). Parameters recorded included maximum pupil diameter (MAX), minimum pupil diameter (MIN), percent constriction (CON), latency (LAT), average constriction velocity (ACV), maximum constriction velocity (MCV), average dilation velocity (ADV) and time to 75% pupil diameter recovery (T75). One measurement was obtained for each eye. Results: The following reference intervals were developed: MAX (6.05-11.30 mm), MIN (3.76-9.44 mm), CON (-37.89 to -9.64 %), LAT (0.11-0.30 s), ACV (-6.39 to -2.63 mm/ s), MCV (-8.45 to -3.75 mm/s), ADV (-0.21-1.77 mm/s), and T75 (0.49-3.20 s). Clinical significance: The reference intervals developed in this study are an essential first step to facilitate future research exploring pupillometry as a pain assessment method 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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".