Total keratometry for determination of true corneal power after myopic small-incision lenticule extraction
Bibliographic record
Abstract
PURPOSE: To gauge the value of total keratometry (TK) to estimate corneal power change in eyes that underwent small-incision lenticule extraction (SMILE) for treatment of myopia or myopic astigmatism. SETTING: Department of Ophthalmology, Ludwig-Maximilians-University, and SMILE Eyes Clinic Munich Airport, Munich, Germany. DESIGN: Prospective cross-sectional trial. METHODS: A total of 40 eyes of 40 patients who had undergone myopic SMILE were enrolled in this prospective study. Total corneal refractive power (TCRP; Pentacam HR) and TK (IOLMaster 700, Carl Zeiss Meditec AG) values were compared with the clinical history method (CHM). The surgically induced changes in TCRP (ΔTCRP) and TK (ΔTK) were also compared with the changes in spherical equivalent on the corneal plane (ΔSEco). RESULTS: Of the 40 eyes analyzed, the correlation between TK and CHM (R2 = 0.91, P < .001) was stronger than that between TCRP and CHM (R2 = 0.87, P < .001). When compared with the CHM, TCRP underestimated corneal power by a mean relative error of 0.59 diopter (D) and TK by 0.17 D. Linear regression analysis of ΔTCRP/ΔTK and the difference between preoperative and postoperative manifest refraction spherical equivalent at the corneal plane (ΔSEco) showed stronger correlation in ΔTK (R2 = 0.88) than that in ΔTCRP (R2 = 0.82). CONCLUSIONS: The findings endorse TK as an accurate measure for corneal power after myopic SMILE.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".