Reply: Automated and subjective refraction with monofocal, multifocal, and EDOF intraocular lenses: review
Bibliographic record
Abstract
We thank Rodriguez-Vallejo et al. for the appreciation for and comments made on our study.1 They underlined the very purposes of our review that are (1) the importance of considering automated refraction only as a starting point for subjective refraction, (2) the risk of inappropriate evaluation made by ophthalmologists who are unaware of the problem, (3) the risk of inappropriate spectacle or surgical treatment, and (4) the lack of information the surgeons are given about the optics of multifocal and EDOF IOLs. To make the ophthalmology community aware about the problem related to multifocal and EDOF IOLs and automated refraction was our main purpose, and the method suggested by Rodriguez-Vallejo et al. provides an excellent and reproducible way to obtain accurate subjective refraction.2 The adoption of defocus curve-based clinical decisions, as also suggested by Rodriguez-Vallejo et al., is certainly a good way to avoid potential errors that may range from wrong spectacle prescription to unnecessary refractive surgery, both eventually leading to patient dissatisfaction. We also believe that subjective refraction should always be included by researchers in paper discussing refractive results with multifocal and EDOF IOLs. It may also improve IOL power selection for the second eye. Neural adaptation is an important key point that we did not discuss in our review. We agree on its importance for some patients, especially in multifocal pseudophakic eyes. Another issue is the value of wavefront refraction that is important although less familiar to ophthalmologists and would require a separate investigation and discussion.
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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 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".