Soft toric contact lens prescribing in different countries
Bibliographic record
Abstract
There have been significant improvements in toric soft contact lens design over the past decade. Data from our international contact lens prescribing survey were mined to assess recent trends in toric soft contact lens fitting. This survey was conducted by sending up to 1000 survey forms to contact lens fitters in Australia, Canada, Japan, the Netherlands, Norway, the UK and the USA each year between 2000 and 2009. Practitioners were asked to record data relating to the first 10 contact lens fits or refits performed after receiving the forms and to return them to us for analysis. The data revealed a gradual increase in the extent of toric soft lens fitting this century. Excluding Japan – which had a consistently low rate of soft toric lens fitting over the survey period – soft toric lenses now represent over 35% of all soft lenses prescribed; it can be assumed that, on average (and again excluding Japan), all cases of astigmatism 0.75 D or less remains uncorrected among contact lens wearers. Toric lenses are fitted more to those who are older, full-time wearers and reusable lens wearers, and less to those wearing silicone hydrogel and extended wear lenses.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.000 |
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".