Bibliographic record
Abstract
Introduction. Gaze-dependent patterns of binocular misalignment are used to diagnose extraocular muscle pareses; however, utilizing video-based eye trackers for this analysis is challenging because erroneous patterns may arise when an observer’s pupil changes size during the experiment. This can introduce bias across time (i.e., the pupil-size artifact) and/or gaze positions (e.g., variable pupil foreshortening errors). The goal of our experiment was to quantify the influence of both phenomena on the repeatability of gaze-estimates and to determine whether pharmacological dilation could reduce any biases present. Methods. The Eyelink 1000 Plus (Tower Mount) was used in conjunction with a bite-bar apparatus to measure the eye position and pupil size of twenty-eight observers. Measurements were repeated at twenty-five gaze positions (10° x 10° grid) across twenty two-minute blocks. Eye positions and pupil sizes were referenced to the first block and then averaged across gaze-positions (for each block) and across blocks (for each gaze position) to determine whether spatiotemporal associations between changes in eye position and changes in pupil size existed. The same analysis was then performed for two observers with dilated pupils. Results. Progressive pupillary constrictions were associated with progressive rightward and leftward drifts in position for the left- and right-eyes, respectively. A progressive upward drift in both eyes was also associated with a sequential reduction in pupil size. On the other hand, the continuous miosis was not associated with any gaze-dependent shifts in eye position. Dilation prevented any sequential change in pupil size from occurring and thus improved the repeatability of gaze-estimates across time. Conclusions. Fluctuations in pupil size contribute to fixational drift; however, this can be minimized by pharmacological dilation which maintains the validity of the initial calibration across time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".