Estimation of corneal power after myopic laser refractive surgery: Comparison of methods against back-calculated corneal power
Bibliographic record
Abstract
PURPOSE: To compare methods of corneal power estimation after myopic laser refractive surgery with back-calculated corneal power (K). SETTING: Private practice, Edmonton, Alberta, Canada. DESIGN: Case series. METHODS: Patients with previous myopic laser surgery followed by cataract extraction were studied. Back-calculated K obtained with the Holladay IOL Consultant was compared with that obtained by the clinical history method (CHM), the modified Maloney method, an adaptation of the Maloney method using individualized Orbscan IIz-derived posterior corneal power values, Orbscan IIz quantitative area topography, and the Gaussian optics formula. A mixed effects linear model was used for analysis. RESULTS: The mean spherical equivalent (SE) before laser treatment was -6.43 diopters (D) ± 3.52 (SD). The estimated means of all methods except those obtained with the CHM, modified Maloney method, 2.0 mm total axial map, 1.5 mm total mean map, and 1.5 mm total optical map were significantly different from the mean of the back-calculated K. Estimates from the 1.5 mm total mean map were generally 0.06 D higher. The 2.0 mm total axial map, modified Maloney method, 1.5 mm total optical map, and CHM underestimated corneal power by 0.11 D, 0.13 D, 0.22 D, and 0.26 D, respectively. Unit increases in optical zone and pre-laser myopic SE were associated with decreases in corneal power of 1.58 D (P = .047) and 0.55 D (P = .0001), respectively. CONCLUSION: The modified Maloney method, 2.0 mm total axial map, 1.5 mm total mean map and 1.5 mm total optical map of the Orbscan IIz may provide estimates closer to the back-calculated K than the CHM. FINANCIAL DISCLOSURE: No author has a financial or proprietary interest in any material or method mentioned.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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) | 0.001 | 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 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".