International trends in prescribing silicone hydrogel contact lenses for daily wear (2000–2023): An update
Bibliographic record
Abstract
PURPOSE: Introduced around the turn of the 21st century, silicone hydrogel contact lenses alleviated hypoxic anterior eye complications due to their high oxygen transmissibility. The purpose of this work is to update earlier surveys by describing international trends in silicone hydrogel daily wear contact lens fitting between 2000 and 2023. METHOD: An annual contact lens prescribing survey was sent to eye care practitioners in up to 71 countries between 2000 and 2023. Data relating to 260,144 daily wear soft contact lens fits undertaken in 20 countries returning reliable longitudinal data were analysed in respect of silicone hydrogel daily wear contact lens fitting. RESULTS: There has been a dramatic increase in silicone hydrogel daily wear lens fits (p < 0.0001), increasing from 2.8 % of all daily wear soft lens fits in 2000 to 73.7 % in 2023. Of all daily wear soft contact lenses prescribed to males, 44.6 % were silicone hydrogel lenses, compared with 43.5 % for females (p = 0.0146). The mean age of those wearing silicone hydrogel daily wear lenses was 32.0 ± 14.5 years, compared to 30.4 ± 13.6 years for those wearing daily wear hydrogel lenses (p < 0.0001). Between 2019-2023, the average percentage of fits was - (a) material type: silicone hydrogel - 73 %; mid-water content hydrogels - 13 %; high water content hydrogels - 9 %; and low water content hydrogels - 5 %, and (b) lens design: spherical - 44 %, toric - 32 %, multifocal - 17 %, monovision - 4 %, and 'other' - 3 %. CONCLUSION: The dramatic increase in silicone hydrogel contact lens prescribing for daily wear has been commensurate with the introduction of multiple lens brands and an ongoing expansion of lens designs, parameters and replacement frequency options. The balance between silicone hydrogel and hydrogel lens prescribing is perhaps starting to approach an equilibrium.
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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 | 0.006 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".