Comparison of ocular biometric measurements and intraocular lens power calculation using different methods in eyes with implantable collamer lenses
Bibliographic record
Abstract
This prospective cohort study included 80 healthy candidates for Implantable Collamer Lens (ICL) implantation who underwent biometric assessments with Scheimpflug imaging (the Pentacam-AXL) and swept-source optical coherence tomography (SS-OCT; the IOLMaster-700), both before and 3 months after surgery. The main outcome measures were mean keratometry, anterior chamber depth, axial length, and various intraocular lens (IOL) calculation formulas (Haigis, SRK/T, Hoffer Q, Holladay 1, Barrett Universal 2, and Olsen). The interchangeability of the devices was assessed by generating 95% limits of agreement (95% LoA) and associated Bland-Altman plots. The average age of the participants was 31.5 ± 5.4 years (22-43), with 58 (72.5%) being female. Among the cases analyzed, 11 (13.4%) had incorrect anterior lens surface segmentation using the IOLMaster-700, and 1 case (1.2%) had inappropriate segmentation using the Pentacam-AXL. Postoperative IOL power calculation resulted in readings that were, on average, 0.15 to 0.30 D higher compared to preoperative measurements. The 95% LoAs could differ by up to 0.85 D higher after surgery, indicating weak agreement between pre- and postoperative measurements. There was poor agreement between the IOLMaster-700 and Pentacam-AXL in IOL power calculation for eyes with post-ICL implantation, with a difference of more than 1 D in the 95% LoAs. In conclusion, Scheimpflug imaging was found to be less susceptible than the SS-OCT technique to segmentation errors of the anterior lens surface after ICL implantation. Neither device showed interchangeable results for pre- versus postoperative IOL power calculation. The determination of IOL power by the IOLMaster-700 versus Pentacam-AXL was not interchangeable in eyes with ICL implantation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".