Quarter of a century of contact lens prescribing trends in Australia (2000–2024)
Bibliographic record
Abstract
CLINICAL RELEVANCE: Knowledge of contact lens prescribing trends can (a) assist practitioners to benchmark their own prescribing habits, (b) help the contact lens industry understand preferred products, and (c) support academics in scholarly writings. BACKGROUND: This work aims to document contact lens prescribing trends in Australia over the past quarter of a century. METHODS: An annual survey of contact lens prescribing trends was conducted in Australia each year from 2000 to 2024, inclusive, by asking optometrists to provide information relating to 10 consecutive contact lens fits undertaken between January and March. RESULTS: Over the 25-year survey period, a total of 20,281 contact lens fits were reported. Lens wearers were 34.4 ± 14.7 years of age and 65% were female. Over the survey period, rigid lenses increased from 13% to 21% of all lens fits. Of all soft lens fits, silicone hydrogel lenses increased from 22% to 96%; soft torics increased from 24% to 30%; daily disposables increased from 7% to 63%; soft extended wear decreased from 16% to 5%; multifocals increased from 5% to 26%; and myopia control (first fit in 2011) reached 3% by 2024. Rigid lens extended wear (primarily orthokeratology) increased from 0.3% to 17% of rigid lens fits, primarily for myopia control. Multi-purpose lens care solutions are ubiquitous, and peroxide systems are seldom prescribed. Rigid lenses and monthly replacement soft lenses are largely worn full time, whereas daily disposables are worn full time and part time in equal measure. Australian and global prescribing trends are broadly consistent. CONCLUSIONS: Contact lens fitting trends this century can be characterised as: soft lenses - significant increase in silicone hydrogels, daily disposables, torics and multifocals; rigid lenses - dominance of high-Dk materials, and recent resurgence in scleral and orthokeratology fits. There is little extended wear fitting. Multi-purpose care systems are ubiquitous.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".