Commentary: Pricing Cataract (and Other Straightforward) Surgeries – A Policy Perspective to Build Capacity, Value and Innovation
Bibliographic record
Abstract
Aligning with Crump and colleagues' (2024) conclusions on cataract surgery, this article champions a level playing field for expanding surgical capacities for straightforward surgeries. It is agnostic toward for-profit or not-for-profit models. It argues for experimenting with new ambulatory facilities to meet urgent needs, emphasizing Ontario's successful two-decade experience with models such as the Kensington Eye Institute. The discussion advances a three-tiered pricing framework, advocating for transparent, structured pricing to reduce wait times and improve public health outcomes. This approach seeks to balance annual commitments, quarterly adjustments and spot market needs, promoting innovation, cost-efficiency and quality care.
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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.117 | 0.074 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".