Wavefront Aberrations in Subjects Wearing Soft Aspheric Contact Lenses and Those Wearing Spherical Ones
Bibliographic record
Abstract
Purpose: To measure the level of higher order aberrations (HOA) when wearing a soft aspheric contact lens (CL), compared to a spherical CL, in myopic subjects. Method: Fifteen myopic subjects aged 20-30 years were tested for the presence of dry eye. Aberrometry measurements were done without a contact lens as well as with a spherical CL and an aspheric CL. Root mean square error (RMS) of HOA, spherical aberration (SA) and coma were measured five times in an interval of 15 seconds without blinking for each of the 3 conditions. Results: Wearing a spherical CL produced a significant increase of SA and horizontal coma compared to an eye without a contact lens. When wearing an aspheric CL, there was a trend towards a smaller increase of these aberrations. However, the difference between both types of lens was not statistically significant. In terms of total HOA, these were higher when wearing the spherical CL, while they tended to be less with the aspheric CL. As for the variations between blinks, there was a similar increase in total HOA and individual modes with time for the three conditions. Conclusion : Wearers of aspheric CL seem to show a tendency towards smaller amounts of SA, horizontal coma and HOA in general in comparison with wearers of SCL. However, total HOA increases during a long interval between blinks, no matter the condition.
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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) | 0.002 | 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".