Impact of Blur on Suprathreshold Scaling of Ocular Discomfort
Bibliographic record
Abstract
PURPOSE: To examine the suprathreshold scaling of pneumatic stimuli, and the ratings of discomfort and intensity under clear and defocused visual conditions. METHODS: Twenty-one participants rated sensory intensity and discomfort of a series of mechanical stimuli from a pneumatic esthesiometer, using a 0 to 100 numerical scale under clear and defocused visual conditions. Esthesiometry was performed on one eye while the fellow eye viewed a 3-m distant 6/60 target through a trial lens. For the clear visual condition, a +0.25DS lens was used over the subject's refractive correction, and for defocus, an additional +4.00DS was used. Central corneal mechanical thresholds were first estimated using ascending methods of limits. Then, stimuli that were 25%, 50%, 75%, and 100% above threshold were presented in random order in three sessions of clear and defocused vision, and subjective ratings were recorded. Power exponents that define the slope of the sensory transducer functions were derived for discomfort and intensity estimates. RESULTS: No significant differences (P = 0.66) in mechanical thresholds, ratings of discomfort (P = 0.54), and intensity (P = 0.30) were observed between the visual conditions. Power exponents for discomfort showed significant differences (P = 0.05) between clear and defocus conditions, but not intensity (P = 0.22). Comparison between discomfort and intensity showed differences in exponents when vision was clear (P = 0.02) and defocused (P < 0.001). CONCLUSIONS: Scaling of suprathreshold pneumatic stimuli varies with viewing conditions. When vision was not clear, the exponent of the average transducer function for discomfort was steeper and this finding is the first demonstration of an association between ocular surface sensation and quality of vision.
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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 | 0.001 | 0.005 |
| 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) | 0.002 | 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".