Changing perspective of reasons for not performing laser‐assisted in situ keratomileusis among candidates in a university eye clinic
Bibliographic record
Abstract
BACKGROUND: The aim was to retrospectively analyse the reasons for not performing laser-assisted in situ keratomileusis (LASIK) surgery among refractive surgery candidates at a university eye clinic. METHODS: Case records of patients who presented to a university eye clinic between June 2005 and June 2010 for consideration for LASIK surgery were examined. Cases that did not undergo LASIK were selected for analysis. Reasons for not performing surgery in these cases were analysed. RESULTS: In total, 552 patients requested LASIK between July 2005 and June 2010 and 377 (68.3 per cent) of them received refractive surgery. Among 175 (31.7 per cent) patients who did not get LASIK, 62 (35.4 per cent) were male and 113 (64.6 per cent) were female, with a mean age at presentation of 36.4 ± 9.3 years (range: 19 to 78 years). The most common reasons for not offering LASIK were low corneal thickness (28.6 per cent), high myopia (15.4 per cent), large pupil (8.0 per cent) and keratoconus (7.4 per cent). Overall, 39 patients (22.3 per cent) changed their mind after their initial consultations with surgeons. The prevalence of rejection of LASIK decreased from 44.1 per cent between July 2005 and June 2006 to 3.5 per cent between July 2009 and June 2010. CONCLUSIONS: Reasons for not performing refractive surgery are quite diverse. Inadequate corneal thickness and change of mind after initial consultation were the most common reasons in the present study. There was a marked change in magnitude and trend of reasons for not performing LASIK over the study period. Further studies from settings other than university hospitals would be beneficial to compare the trend in patient selection.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".