Anomalies in the Prescribing of Soft Contact Lens Power
Bibliographic record
Abstract
OBJECTIVES: To determine the proportion of prescribed soft lenses rounded to the nearest half diopter and any variations from country to country and between lens types. METHODS: Marketing data were obtained for soft lenses supplied during a 1-year period for lenses representing each of the following categories: mid-water hydrogel (MWH), silicone hydrogel, daily disposable, and toric silicone hydrogel (TSH). The data were analyzed for several countries/regions. Spherical lenses were analyzed in the range 1.00 to 5.75 D for plus and minus powers, and toric lenses in the range 0.50 to 5.75 D. This ensured a similar number of lenses in full or half diopter powers were compared with quarter and three-quarter diopter powers, and that there was no enforced rounding due to nonavailability of powers. By comparing the proportion of lenses from the 2 power groups, the proportion of lenses rounded to the nearest half diopter was estimated. It was assumed that half the difference between the totals of the 2 power groups represented those lenses dispensed to the nearest half diopter and, therefore, dispensed inaccurately; this was termed the "rounding rate" (RR). RESULTS: The power distribution curve for the sphere powers spiked in half diopter steps, illustrated a bias toward prescribing full and half diopter powers. With all lenses, the RR varied widely between countries. For the MWH, this ranged from 1.7% (Canada) to 11.6% (Iberia). The RRs were 2 to 3 times higher for plus than minus power lenses, however, this also varied by country. Overall, the RRs were lower for the silicone hydrogel and daily disposable contact lenses compared with the MWH, in particular for France and Iberia. The TSH results showed the greatest consistency between countries, with RRs ranging from 3.9% (Germany) to 9.5% (Rest of Europe). Most countries showed similar or lower RRs for TSH compared with MWH although, for some countries (e.g., United Kingdom, Nordic), these were higher. There was less difference in RRs for TSH lenses between plus and minus spheres. CONCLUSION: A surprising proportion of soft lenses are prescribed to the nearest half diopter, although this varies according to lens type. There are also considerable variations between countries, presumably due to differences in training, fitting practices, and supply routes. These findings suggest that there is widespread room for improvement in the prescribing accuracy of soft contact 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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".