International trends in prescribing extended wear soft contact lenses (2000–2023): An update
Bibliographic record
Abstract
PURPOSE: Extended wear has long been considered as the ultimate contact lens modality in terms of user convenience. The purpose of this work is to update earlier surveys by describing international trends in extended wear soft lens fitting between 2000 and 2023, inclusive. METHOD: An annual contact lens prescribing survey was sent to eye care practitioners in up to 71 countries between 2000 and 2023, inclusive. Data relating to 282,142 soft contact lens fits undertaken in 20 countries returning reliable longitudinal data were analysed in respect of extended wear soft lens fitting. RESULTS: Over the duration of the work there was a very small decrease in the prescribing of extended wear soft lenses (p < 0.0001). More detailed inspection shows that prescribing of these lenses steadily increased from 5.8 % of all soft lens fits in 2000 to 11.6 % in 2007, then steadily decreased to 5.2 % in 2023. Of all soft contact lenses prescribed to males, 9.2 % were fitted for extended wear, compared with 6.7 % for females (p < 0.0001). The mean age of extended wear soft lens wearers at fitting was 34.7 ± 14.7 years, compared to 31.1. ± 14.10 years for daily soft lens wearers (p < 0.0001). Analysis of 1,948 recent extended wear soft lens fits (2019-2023, inclusive), in terms of material type, revealed that, on average, 86 % and 14 % of extended wear soft lenses were fitted using silicone hydrogel and hydrogel materials, respectively. CONCLUSION: A modest increase in extended wear soft lens prescribing from 2000 to 2007 corresponded with the introduction of high oxygen transmissibility silicone hydrogel lenses. However, prescribing of this lens type declined thereafter, probably due to ongoing concerns over their increased rate of microbial keratitis, resulting in a prescribing rate in 2023 (5.2%) that was little different to that observed in 2000 (5.8%).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".