Normal clinical electroretinography parameters for poodle, Labrador retriever, Thai ridgeback, and Thai Bangkaew
Bibliographic record
Abstract
The purpose of the present study was to establish normal electroretinogram (ERG) parameters using 56 normal eyes of four dog breeds common in Thailand: poodle, Labrador retriever, Thai ridgeback, and Thai Bangkaew. Standard ERG findings were bilaterally recorded using a handheld multi-species ERG unit with an ERG-jet lens electrode for 28 dogs under preanesthesia with diazepam, anesthesia with propofol, and anesthesia maintenance with isoflurane. There were significant differences in the mean values of ERG amplitudes and implicit times among the four dog breeds (p < 0.05) except for the b-wave implicit time of the photopic 30 Hz flicker response with 3 cd.s/m(2) (p = 0.610). Out of the four breeds, Thai Bangkaew had the longest implicit time (p < 0.001) of scotopic low intensity responses, b-wave of scotopic standard intensity responses (3 cd.s/m(2)), a-wave of the higher intensity response (10 cd.s/m(2)), and a-wave of the photopic single flash response (3 cd.s/m(2)). For the b/a ratio, only the ratio of the Cone response was significantly different among the different breeds. In this summary, normal ERG parameters for four dog breeds were reported. Data from the investigation supported the hypothesis that determination of breed-specific limits of normality for ERG responses is necessary for individual clinics and laboratories.
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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 | 0.000 | 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".