Regional corneal biomechanics assessment as a function of age using Strain-Stress Index maps.
Bibliographic record
Abstract
Purpose To gain a better understanding of corneal Stress-Strain Index (SSI) maps in healthy eyes and to determine their changes with age.Method The eyes of 72 participants (age 43.1 ± 20.9 years, 69.5% female) were included in the analysis, considering the left and right eyes separately. Corneal biomechanics were assessed using a combination of the Pentacam and Corvis instruments, whose data enabled the production of the SSI maps. The corneas were divided into nine zones to facilitate zone-specific analysis of biomechanical behaviour. Regression analyses were conducted to evaluate the relationship between age and SSI values in each corneal zone.Results The mean overall SSI value of the cornea for all participants was 1.075. Considering different age groups, significant differences in SSI were seen between the young and older groups in the overall map from 0.938 ± 0.067 in 20-50-year-olds to 1.143 ± 0.064 in 50-80-year-olds (ANOVA, p < 0.001). The same effect was seen for each zone separately (ANOVA, p < 0.001). The corneal central apex and peripheral zones showed higher mean SSI values (1.153 ± 0.079) and hence, higher corneal stiffness compared with paracentral zones (0.890 ± 0.057).Conclusion This paper showcases a series of maps depicting corneal elasticity and explores the differences in corneal stiffening with age across various regions of healthy corneas. The results reveal that stiffening tends to accelerate in areas that are already stiffer and decelerate in weaker regions. This deeper insight into ocular physiology could enhance clinical care by enabling more personalised treatments based on the patient's age and the specific corneal regions being addressed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".