International trends in prescribing multifocal and monovision soft contact lenses to correct presbyopia (2000–2023): An update
Bibliographic record
Abstract
PURPOSE: Numerous multifocal soft contact lenses have been introduced into clinical practice over the past half century. The purpose of this work is to update earlier surveys by describing international trends in multifocal and monovision soft lens fitting for presbyopia between 2000-2023, inclusive. METHOD: An annual contact lens prescribing survey was sent to eye care practitioners in up to 71 countries between 2000-2023. Data relating to 52,580 soft daily wear lens fits to presbyopes (those ≥45 years of age) undertaken in 20 countries returning reliable longitudinal data were analysed in respect of multifocal and monovision soft daily wear lens fits. RESULTS: Overall, multifocal and monovision soft daily wear lens prescribing to presbyopes has more than doubled over the course of this survey, from 26.4 % of standard soft daily wear lens fits in 2000 to 61.1 % in 2023 (p < 0.0001). There were significant differences between countries in presbyopia soft daily wear lens prescribing (p < 0.0001). Of all soft daily wear fits to males, 45.1 % were multifocal and monovision soft lenses, compared with 52.7 % for females (p < 0.0001). When considered as the proportion of lenses fitted by age, multifocal soft lens fitting peaked between 50-65 years, followed by a precipitous drop until 85-90 years of age, and then an increase beyond 90 years of age. Analysis of 13,014 recent soft lens fits to presbyopes (2019-2023) revealed the following fitting proportions: multifocal lenses - 51 %; monovision - 10 %; and non-presbyopia fitting - 39 %. CONCLUSION: There has been a substantial increase in soft contact lens correction of presbyopia using multifocal and monovision corrections throughout the 24 years of this survey. A significant number of soft contact lens-wearing presbyopes are not receiving a presbyopia contact lens correction.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".