International trends in prescribing toric soft contact lenses to correct astigmatism (2000–2023): An update
Bibliographic record
Abstract
PURPOSE: There have been significant advancements in toric soft contact lens design and manufacturing technology, and increased product availability, over the past half a century. The purpose of this work is to update earlier surveys by describing international trends in toric 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 220,934 standard soft daily wear single vision lens fits undertaken in 20 countries returning reliable longitudinal data were analysed in respect of toric soft lens fitting. RESULTS: Overall, toric soft lens prescribing almost doubled over the time-course of this survey, from 24.4 % of standard soft daily wear single vision lens fits in 2000 to 46.2 % in 2023 (p < 0.0001). There were significant differences between countries in toric soft lens prescribing (p < 0.0001). Of all standard soft daily wear single vision contact lenses prescribed to males, 32.0 % were toric soft lenses, compared with 28.7 % for females (p < 0.0001). The mean age of toric soft lens wearers was 30.5 ± 12.5 years, compared to 27.9. ± 12.1 years for spherical soft lens wearers (p < 0.0001). Analysis of 13,582 recent toric soft lens fits (2019-2023, inclusive), in terms of material type and replacement frequency, revealed the following proportions: reusable silicone hydrogel - 51 %; daily disposable silicone hydrogel - 27 %; daily disposable hydrogel - 12 %; and reusable hydrogel - 10 %. CONCLUSION: There has been a substantial increase in toric soft lens fitting throughout the 24 years of this survey, to a point whereby almost all clinically significant astigmatism is being corrected among those wearing standard soft daily wear single vision lenses.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".