Trends in Australian contact lens prescribing during the first decade of the 21st Century (2000–2009)
Bibliographic record
Abstract
Purpose: The aim was to document contact lens prescribing trends in Australia between 2000 and 2009. ---------- Methods: A survey of contact lens prescribing trends was conducted each year between 2000 and 2009. Australian optometrists were asked to provide information relating to 10 consecutive contact lens fittings between January and March each year. ---------- Results: Over the 10-year survey period, 1,462 practitioners returned survey forms representing a total of 13,721 contact lens fittings. The mean age (± SD) of lens wearers was 33.2 ± 13.6 years and 65 per cent were female. Between 2006 and 2009, rigid lens new fittings decreased from 18 to one per cent. Low water content lenses reduced from 11.5 to 3.2 per cent of soft lens fittings between 2000 and 2008. Between 2005 and 2009, toric lenses and multifocal lenses represented 26 and eight per cent, respectively, of all soft lenses fitted. Daily disposable, one- to two-week replacement and monthly replacement lenses accounted for 11.6, 30.0 and 46.5 per cent of all soft lens fittings over the survey period, respectively. The proportion of new soft fittings and refittings prescribed as extended wear has generally declined throughout the past decade. Multi-purpose lens care solutions dominate the market. Rigid lenses and monthly replacement soft lenses are predominantly worn on a full-time basis, whereas daily disposable soft lenses are mainly worn part-time.---------- Conclusions: This survey indicates that technological advances, such as the development of new lens materials, manufacturing methods and lens designs, and the availability of various lens replacement options, have had a significant impact on the contact lens market during the first decade of the 21st Century.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".