Disagreement Between Theoretical and Actual Phorcides Outcomes: Is Phorcides Inferior to Treating on the Manifest Refraction? [Letter]
Bibliographic record
Abstract
Avi Wallerstein,1,2,* Mathieu Gauvin1,2,* 1Department of Ophthalmology and Visual Sciences, McGill University, Montreal, QC, Canada; 2Department of Research & Development, LASIK MD, Montreal, QC, Canada*These authors contributed equally to this workCorrespondence: Avi WallersteinMD Level, 1250 Rene-Levesque Blvd W, Montreal, QC H3B 4W8, CanadaTel +1 514-908-9888 Ext 2273Email awallerstein@lasikmd.com\nWe read with interest “Topography-Guided Refractive Astigmatism Outcomes: Predictions Comparing Three Different Programming Methods.”1 In the Manifest group, the calculated theoretical outcomes revealed an unexpectedly elevated postoperative refractive astigmatism average error of 0.56 ± 0.22 D.1 This large amount of theoretical postoperative cylinder was not replicated in a recent real-world clinical study by the same authors, comparing actual topography-guided LASIK outcomes in Phorcides versus manifest-treated eyes.2 The reported empirical postoperative astigmatism error average in the Manifest group was as  low as 0.15 D, fourfold better than that reported in the current theoretical outcomes study.1\n \nView the original paper by Stulting and colleagues\n 
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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 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".