Costs of treating cataract surgery complications from a US provider perspective: a micro-costing approach for health economic analyses
Bibliographic record
Abstract
Introduction Health economic analyses of novel technologies are useful for purchasing decisions and should consider adverse events if technologies demonstrate an improvement in safety. However, the costs associated with cataract surgery complications from a healthcare provider perspective are not well documented in the literature.Areas covered This review discusses several cataract surgery complications that can potentially be impacted by novel technologies including posterior capsule rupture (PCR), corneal burn, cystoid macular edema (CME), corneal edema, and elevated intraocular pressure (IOP). Limitations of databases to identify these costs from a healthcare provider perspective are discussed. Estimates of the healthcare resources utilized for treating these cataract surgery complications and associated costs are presented, derived from discussions with an expert panel and published literature, where available.Expert opinion There is a current gap in the literature with respect to the costs of cataract surgery complications to US healthcare providers. Estimates from this paper can provide insight into complications and associated costs that could be considered in future economic analyses of technologies designed to reduce cataract surgery complications. Those results in turn can inform healthcare resource planning and allocation decisions.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".