Comparison of Barrett Toric Calculations Using Measured and Predicted Posterior Corneal Astigmatism in Cataract Surgery Patients
Bibliographic record
Abstract
Aim: To compare residual astigmatism prediction errors across Barrett toric calculations using predicted posterior corneal astigmatism (PCA) and PCA measured using the IOL Master 700 with total keratometry (IOLM). Methods: A retrospective cohort study was undertaken on patients with corneal astigmatism and no other ocular comorbidities that underwent uneventful refractive femtosecond laser-assisted cataract surgery with toric IOL implantation between May 2019 and November 2019. Toric calculations were performed using the Barrett toric calculator and the following values: predicted PCA with anterior corneal measurements from Pentacam, IOLM standard keratometry (SK), OPD scan, and median measurements from these devices; predicted PCA with IOLM total keratometry (TK); and measured PCA with IOLM SK or IOLM TK. Residual astigmatism prediction error was calculated for each device and method of calculation at postoperative month 1 and 3 using the astigmatism double angle plot tool. Results: A total of 24 eyes, 10 with-the-rule (WTR), 10 against-the-rule (ATR) and 4 oblique astigmatism, from 24 patients were included in this study. PCA ranged from 0.00 to 0.67 D with a mean of 0.24 ± 0.15 D in all eyes. PCA was significantly greater in WTR eyes (0.32 D) compared to ATR eyes (0.16 D; p < 0.05). In ATR eyes, calculations made using IOLM SK and measured PCA had significantly lower total corneal astigmatism and toric IOL cylinder power compared to calculations made using Pentacam and IOLM TK (p < 0.05). No significant difference in mean absolute or centroid residual astigmatism prediction error was observed across devices or calculation methods. The percentage of eyes with absolute astigmatism prediction errors ≤0.5 D was not significantly different across groups. Conclusion: Barrett toric calculations using predicted PCA and PCA measured using IOLM produced comparable residual astigmatism prediction errors. The incorporation of median measurements did not significantly impact calculation accuracy.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".