Vector Analysis Reveals That Topography-Guided LASIK Targeting the Manifest Refraction (MR) is Superior to Topography-Modified Refraction (TMR) and Layer Yolked Reduction of Astigmatism (LYRA) [Letter]
Bibliographic record
Abstract
A Comparative Study Comparing Treating the Manifest versus the Topographic Astigmatism". 1 We commend the authors for performing vector analysis of surgical astigmatic changes using the standardized Alpins Method for their outcomes analysis paper.However, the papers' conclusion is not supported by the vector analysis findings.Astigmatism vector analysis answers the following clinical questions: 1 -Was the attempted astigmatic treatment undercorrected or overcorrected? 2 -Was the under/overcorrection due to the magnitude or axis of treatment?3 -Was there a consistent axis error?Vector analysis is therefore essential for a complete evaluation of excimer surgical outcomes and for determining optimal nomogram adjustments to improve future outcomes.Refractive surgery journals have made this type of analysis the standard for reporting.2 The Alpins difference vector (DV) represents the vectorial difference, in diopters (D), between the desired target surgical treatment of astigmatism (TIA) and the achieved treatment that was induced (SIA).DV is small when the treatment is accurate and large when the treatment is inaccurate.The DV provides the most valuable statistical basis for comparing multiple surgical treatment options.Aboalazayem et al compared treating the manifest refractive astigmatism (Manifest group), vs the anterior corneal astigmatism with spherical adjustment (Full TMR group), vs the anterior corneal astigmatism without spherical adjustment (Partial TMR group). 1 They concluded that treating the anterior corneal astigmatism is best.Yet their data shows superior vector analysis outcomes in the Manifest group, where the difference vector (DV) was as low as 0.20 D, compared to 0.90 D in the Full TMR group, or 0.50 D in the Partial TMR group.These results indicate that eyes in the TMR group and Partial TMR group had by far the greatest astigmatism treatment errors postoperatively in this comparative study.It is hard to reconcile how these groups are presented as having better vision.In addition, the coefficient of determination (R 2 ) between the TIA and SIA was 0.82 in the Manifest
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.005 | 0.001 |
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".