Wavefront-Guided Contact Lens Corrections – Increasing Choice for the Individual with Keratoconus
Bibliographic record
Abstract
If one were to ask an individual with keratoconus what need they hope to meet with an optical correction, the response would be as varied as the number of individuals diagnosed with the disease. Keratoconus impacts individual patients in myriad ways, and different aspects (or dimensions) of the correction are important to each individual patient. From our work in the laboratory, several recurring, and at times competing, dimensions have come to the forefront. For example, visual and optical performance may be of the utmost importance for one patient, while comfort and an ability to wear the lenses for the majority of waking hours may be paramount for another. Given that no single correction can meet the needs of every individual with keratoconus (just as no single correction can meet the needs of the typical population) a pressing need in regards to optical correction for the individual with keratoconus can be summarized in two words: increased choice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".