Variation in Prices Charged to Patients for Specialty Intraocular Lenses Inserted during Universally Covered Cataract Surgery
Bibliographic record
Abstract
BACKGROUND: Patients often pay for specialty intraocular lenses (IOLs) for cataract surgery covered by universal insurance. This practice creates the potential for inequitable pricing where the medical service provider is also the retailer. We measured the variation in prices between cataract surgeons for the same IOL and associated testing. METHODS: We telephoned every cataract surgeon in Ontario, Canada, and asked their price for the most common type of specialty IOL as a prospective patient. We measured the total prices quoted and variation between providers. RESULTS: We contacted 404 ophthalmologists. There were 256 that performed cataract surgery but 127 offered the most commonly employed specialty IOL and would provide a price to patients over the telephone. We obtained prices from all 127 ophthalmologists. Prices for the same lens and associated testing varied substantially between ophthalmologists from $358 to $2790 (median $615, interquartile range $528-$915). There was variation in all components of the total out-of-pocket price, including the price for the IOL itself, charges for uninsured eye measurements, and non-specific supplemental fees. CONCLUSION: Although cataract surgery is covered by public health insurance, some ophthalmologists charge much more than others for the same specialty IOL and associated testing. Greater access to price information and better regulatory control could help ensure patients receive fair value for out-of-pocket health expenses.
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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 | 0.018 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".