Probability Model of the Inaccuracy of Residual Stromal Thickness Prediction to Reduce the Risk of Ectasia After LASIK Part II: Quantifying Population Risk
Bibliographic record
Abstract
PURPOSE: To derive a statistical model to estimate the rate of excessive keratectomy depth below a selected cut-off residual stromal thickness (RST) given a minimum target RST and specific Clinical Protocol; apply the model to estimate the RST below which ectasia appears likely to occur and back-calculate the safe minimum target RST that should be used given a specific Clinical Protocol. METHODS: Myopia and corneal thickness distribution were modeled for a population of 5212 eyes that underwent LASIK. The probability distribution of predicted target RST error (Part I) was used to calculate the rate of excessive keratectomy depth for this series. All treatments were performed using the same Clinical Protocol; one surgeon, Moria LSK-One microkeratome, NIDEK EC-5000 excimer laser, Orbscan pachymetry, and a minimum target RST of 250 microm--the Vancouver Clinical Protocol. The model estimated the RST below which ectasia appears likely to occur and back-calculated the safe minimum target RST. These values were recalculated for a series of microkeratomes using published flap thickness statistics as well as for the Clinical Protocol of one of the authors-the London Clinical Protocol. RESULTS: In the series of 5212 eyes, 6 (0.12%) cases of ectasia occurred. The model predicted an RST of 191 microm for ectasia to occur and that a minimum target RST of 329 microm would have reduced the -rate of ectasia to 1: 1,000,000 for the Vancouver Clinical Protocol. The model predicted that the choice of microkeratome varied the rate of ectasia between 0.01 and 11,623 eyes per million and the safe minimum target RST between 220 and 361 microm. The model predicted the rate of ectasia would have been 0.000003: 1,000,000 had the London Clinical Protocol been used for the Vancouver case series. CONCLUSIONS: There appears to be no universally safe minimum target RST to assess suitability for LASIK largely due to the disparity in accuracy and reproducibility of microkeratome flap thickness. This model may be used as a tool to evaluate the risk of ectasia due to excessive keratectomy depth and help determine the minimum target RST given a particular Clinical Protocol.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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 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".