Test-Retest Reliability of Hemifield, Central-Field, and Full-Field Chromatic Pupillometry for Assessing the Function of Melanopsin-Containing Retinal Ganglion Cells
Bibliographic record
Abstract
PURPOSE: We evaluated the test-retest reliability of current methods of inducing the melanopsin-driven postillumination pupil response (PIPR) under hemifield, central-field, and full-field stimulation conditions. METHODS: Pupil response was recorded with an eye tracker in 10 visually normal participants. Light stimuli were presented using a Ganzfeld screen with a custom-built device that allows specific regions of the retina to be stimulated. Blue light stimulation at 400 cd/m(2) intensity was presented for 400 ms to the lower and upper halves of the central 30° fields (hemifields), central 30° field (central-field), and full-field to induce PIPR. Red light full-field stimulation also was presented with the same intensity and duration as a control condition. Test-retest reliability of the PIPR measures was assessed by calculating the intraclass correlation coefficient (ICC) of six repetitions for lower and upper hemifield stimulation, and three repetitions for central-field and full-field stimulation. RESULTS: Hemifield, central-field, and full-field blue light stimulation induced increasingly greater PIPR in ascending order, while full-field red light stimulation induced no PIPR. Mean lower and upper hemifield PIPR was highly symmetric. Mean ICC of blue light PIPR was 0.87 for lower hemifield, 0.88 for upper hemifield, 0.95 for central-field, and 0.94 for full-field stimulation. CONCLUSIONS: We validated a new and repeatable method to measure PIPR induced by hemifield, central-field, and full-field light stimulation. Good PIPR measurement reliability was obtained under all conditions. This practical and reliable protocol will facilitate the clinical application of PIPR testing in different disease populations.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".