Accuracy and predictability of intraocular lens power calculation after photorefractive keratectomy
Bibliographic record
Abstract
PURPOSE: To investigate the accuracy and predictability of intraocular lens (IOL) power calculation in postoperative photorefractive keratectomy (PRK) eyes. SETTING: Gimbel Eye Centre, Calgary, Alberta, Canada. METHODS: The results in 5 cataract surgery eyes that had had PRK were analyzed retrospectively. Target refractions based on actual and refraction-derived keratometric values were compared with postoperative achieved refractions. The target refractions calculated using 5 IOL formulas and 2 A-constants were also compared with the achieved refractions. RESULTS: In postoperative PRK eyes, the power calculation was more accurate and predictable when the smaller of either the actual or refraction-derived keratometric value was used to calculate the IOL power. The difference between target and achieved refractions appeared smaller when the Binkhorst formula was used. No significant hyperopic shift was observed after cataract surgery. CONCLUSION: The smaller of the actual or the refraction-derived keratometric value is recommended for calculating IOL power in post-PRK eyes.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".