Refractive power profiles of commercially available soft multifocal contact lenses for myopia control
Bibliographic record
Abstract
Abstract Purpose Lens power profiles can provide valuable insights on the imposed optical defocus and visual experience of contact lens wearers, especially in the context of myopia control. This study measured the refractive power profiles of multifocal soft contact lenses (MFCLs) currently used or that have the potential for use in myopia control using high spatial resolution aberrometry. The instrument's repeatability for determining MFCLs power profiles was also assessed. Method The power profiles of 10 MFCLs of various designs (centre‐distance, centre‐near and extended depth of focus) were measured using the Lambda‐X NIMO EVO , a phase shifting Schlieren‐based device. Power profiles were graphically expressed as measured power at each chord position and the maximum add power was calculated. The repeatability of the NIMO EVO was expressed as the within‐subject standard deviation at each chord position for a subset of five MFCLs. Results The measured distance powers differed from nominal powers for more than half of the MFCLs with a definable distance zone. There were variations in the chord position of the distance and near correction zones, rate of power transitions and calculated maximum add between the MFCLs which did not depend on lens design. For half of the MFCLs, the power profile shape was inconsistent between different nominal back vertex powers of the same design. The repeatability of the NIMO EVO was dependent on the lens design, with designs featuring faster rates of power change exhibiting worse repeatability. Conclusions Significant differences in MFCL power profiles were found which were not adequately represented in labelling. This is likely due to the small number of parameters used to define lens power characteristics. Eye health care practitioners should be aware of potential differences in power profiles between different MFCLs, which will impact the retinal defocus introduced during lens wear and the wearer's visual experience.
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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 | 0.001 | 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) | 0.001 | 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".