Change in keratometry after myopic laser in situ keratomileusis and photorefractive keratectomy
Bibliographic record
Abstract
PURPOSE: To compare the change in keratometry (K), spherical equivalent (SE), and visual acuity after myopic laser in situ keratomileusis (LASIK) and photorefractive keratectomy (PRK). SETTING: Academic tertiary care center. DESIGN: Retrospective review. METHODS: The postoperative K, SE, and uncorrected and corrected distance visual acuities were measured 6 months, 9 months, 1 year, 2 years, 3 years, 4 to 5 years, 6 to 7 years, and 8+ years postoperatively. A difference (Δ) for each variable was calculated from its 6-month postoperative baseline. The rates of change were grouped based on the magnitude of myopic correction (0.00 to 2.99 diopters [D]; 3.00 to 5.99 D; 6.00 to 8.99 D), type of surgery (LASIK versus PRK), and age (<34 years; 34 to 45 years; >45 years). RESULTS: Statistically significant differences were found in the rates of change between low and moderate corrections to high corrections for ΔKavg (P=.0472 and P=.0091, respectively) and ΔSE (both P<.0001). Statistically significant differences were found in the rate of change in ΔKavg between all 3 ages groups (P=.0330, P=.0051, and P<.0001) and in ΔSE between ages less than 34 years and 34 to 45 years to ages over 45 years (P=.0158 and P=.0015, respectively). There was no significant difference in the rate of change in ΔKavg and ΔSE between LASIK and PRK (P=.3599 and P=.9403, respectively). CONCLUSION: There was keratometric and refractive regression for myopic LASIK, with the rate of regression depending on treatment magnitude and age. FINANCIAL DISCLOSURE: No author has a financial or proprietary interest in any material or method mentioned.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".