The Behaviour of Contaflex Soft Contact Lens Material During Hydration
Bibliographic record
Abstract
The aim of this study was to quantitatively evaluate the swelling and transparency behaviour of Contaflex soft contact lens materials with varying water-content (38-77%) using high-resolution digital imaging and infrared LiDAR. Contaflex materials with 38%, 55%, 58%, 67% and 77% nominal water-contents, denoted as C38, C55, C58, C67, and C77, were tested. Hydrogel samples (N = 5 per group) were monitored over 24 h in pH 7.1 phosphate-buffered saline. Dimensional changes were assessed via linear and radial expansion factors (LEF and REF), and transparency was tracked during hydration. All groups exhibited rapid initial swelling followed by continued expansion. LEF and REF values increased with water-content; C77 reached LEF and REF values of 1.563 ± 0.093 and 1.536 ± 0.052, while C38 stabilised near 1.201 ± 0.019 and 1.179 ± 0.011, respectively. Refractive index decreased with hydration, from 1.552 in C38 to 1.372 in C77. Power simulations revealed deviations beyond ISO tolerance limits in most materials, particularly those with higher water-content. Transparency changes were consistent with swelling dynamics. These findings support the need for material-specific design adjustments to account for hydration-related dimensional and optical changes in soft contact lenses.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".