Refractive surgery or contact lenses – how and when to decide?
Bibliographic record
Abstract
Kunyong Xu1, Vishal Jhanji2 1Michael G DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada; 2Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong Abstract: Correction of refractive errors can be achieved with spectacles, contact lenses, and refractive surgery. The past decade has seen a surge in the availability of alternatives for patients and surgeons in terms of both surgical and nonsurgical options for the management of refractive errors. Newer generation contact lenses provide enhanced safety and better handling, whereas modern-day refractive surgery presents a plethora of choices based on the clinical characteristics and requirements of patients. We have moved from an era of "one size fits all" to a purely customized way of treating patients with refractive errors. This review presents the background, advantages, and disadvantages of the two most commonly used options for correction of ametropia, ie, contact lenses and refractive surgery. Keywords: laser-assisted in situ keratomileusis, contact lens, patient selection, complications, outcomes
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".