The changing performance of multifocal contact lenses compared to monovision
Bibliographic record
Abstract
Purpose - Multifocal contact lenses (MCLs) have been available for decades. A review of the literature suggests that while, historically, these lenses have been partially successful, they have struggled to compete with monovision (MV). More recent publications suggest that there has been an improvement in the performance of these lenses. This study set out to investigate whether the apparent improved lens performance reported in the literature is reflected in clinical practice. Methods - Data collected over the last 5yrs via the International Contact Lens Prescribing Survey Consortium was reviewed for patients over the age of 45yrs. The published reports of clinical trials were reviewed to assess lens performance over the time period. Results - Data review was of 16,680 presbyopic lens fits in 38 countries. The results are that 29% were fit with MCLs, 8% MV and 63% single vision (SV). A previous survey conducted in Australia during 1988-89 reported that 9% of presbyopes were fit with MCLs, 29% MV and 63% SV. The results from our survey for Australia alone were 28% (MV 13%) vs 9% (MV 29%) suggesting an increase in usage of MCLs from 1988-89 to 2010. A review of the literature indicates the reported level of visual acuities with MCLs in comparison to MV has remained equivalent over this time period, yet preference has switch from MV to MCLs. Conclusions - There is evidence that currently more MCLs than MV are being fit to presbyopes, compared to 1988-89. This increased use is likely due to the improved visual performance of these lenses, which is not demonstrated with acuity measures but reported by wearers, suggesting that patient-based subjective ratings are currently the best way to measure visual performance.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".