New Technologies to Assess Lens-Mediated Effects of the Cornea
Bibliographic record
Abstract
Contact lenses can affect the cornea in a variety of ways. Corneal structure can be altered so that its thickness changes to involve the epithelium and the stroma. As a result, the curvature may be affected, but whether it is the front or the back surface that is affected depends on the type of lens used. If thickness increases sufficiently, corneal transparency may decrease. Contact lenses can also affect cellular structure of all layers of the cornea through mechanical trauma, hypoxia, or toxicity from solutions that are used in association with lenses. More serious complications, such as inflammation and infection, can arise. All these changes can be detected by clinicians using slitlamp biomicroscopes and keratometers if the changes are significant enough. Since the development of computers, optical instruments have become more sophisticated and have enabled the detection of subtle changes but have also facilitated more precise measurement of these conditions along with the ability to capture images of the alterations or defects. This article describes some of the newer techniques and, specifically, the application of optical coherence tomography, confocal microscopy, and esthesiometry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".