Comparative Analysis of Tear Proteins in Keratoconic Scleral Lens Wearers with Variation in Limbal Clearance
Bibliographic record
Abstract
SIGNIFICANCE: Cytokine and protease analysis revealed relative changes in the post-lens tear film of scleral lenses with low and high limbal clearances. Results from this study indicate that midperipheral lens fit is an important fitting feature that can impact the inflammatory response of a keratoconic eye. PURPOSE: The purpose of this study was to investigate changes in levels of inflammatory mediators in the post-lens tear film of keratoconic scleral lens wearers with varying limbal clearance designs. METHODS: Twenty-two keratoconic eyes were fitted with two sets of scleral lenses that were consistent in lens diameter and central sagittal depth but varied in limbal clearance by approximately 50 μm. Lenses were worn in a randomly assigned order for a 2-week period each. At each follow-up visit, immediately after lens removal, tear samples were collected with a microcapillary tube (10 μL, 0.5 mm in diameter) from the bowl of the inverted scleral lens. Tear cytokine and protease analysis was performed using a multiplex electrochemiluminescent array (Meso Scale Discovery, Rockville, MD) instrument. Levels of interleukins 1, 6, and 8; tumor necrosis factor α; and matrix metalloproteinases 1 and 9 were compared and analyzed. RESULTS: Levels of interleukin 1β, tumor necrosis factor α, and matrix metalloproteinase 1 increased with high limbal clearance (P = .01, .006, and .02, respectively). No change in interleukins 6 and 8 levels was found (P > .05). A decrease in matrix metalloproteinase 9 was noted in post-lens tear film of scleral lenses with high limbal clearance (P = .10). DISCUSSION: Relative changes in the cytokine and protease levels were found when comparing low and high limbal clearance, indicating that the midperipheral lens fit is an important feature that can impact the inflammatory response of the keratoconic eye.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| 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".