Wang-Koch formula for optimization of intraocular lens power calculation: Evaluation at a Canadian center
Bibliographic record
Abstract
PURPOSE: To externally validate the Wang-Koch method for optimization of intraocular lens (IOL) formulas. SETTING: TLC Laser Eye Centre, Mississauga, Ontario, Canada. DESIGN: Retrospective case series. METHODS: Consecutive cataract patients with an axial length (AL) of 25.0 mm or longer were recruited. The predicted postoperative spherical equivalents (SEs) calculated from the Holladay 1 formula were compared with the 3-week postoperative SEs to yield prediction errors for Wang-Koch adjusted and unadjusted ALs. A mixed linear model was used to compare the proportion of eyes with a prediction error of ±0.25 diopter (D) or worse, ±0.50 D or worse, and ±1.00 D or worse between groups. The secondary outcomes of mean absolute error and median absolute error were also analyzed. A subgroup analysis was performed based on AL subgroups. RESULTS: Two hundred sixty-two eyes were selected for inclusion with a balanced sex distribution, a mean age of 62.49 years ± 9.13 (SD), and a preoperative AL of 26.49 ± 1.10 mm. Subgroup prediction error comparisons of ±0.50 D or worse favored unadjusted eyes with ALs between 25.0 mm and 26.0 mm (n = 105; P < .001), no difference in eyes with ALs between 26.0 mm and 27.0 mm (n = 91; P = .43), adjusted eyes with ALs between 27.0 mm and 28.0 mm (n = 36; P = .003), and adjusted eyes with ALs of 28.00 mm or longer (n = 30; P < .001). CONCLUSION: The Wang-Koch adjustment should only be applied in eyes with ALs longer than 27.0 mm that have IOL power calculation with the Holladay 1 formula.
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 | 0.001 | 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) | 0.000 | 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".