Comparison of Surgically Induced Astigmatism and Corneal Morphological Features Between Femtosecond Laser and Manual Clear Corneal Incisions
Bibliographic record
Abstract
PURPOSE: To assess corneal morphologic changes and surgically induced astigmatism (SIA) following clear corneal incisions (CCIs) created manually or with the Catalys femtosecond laser (Johnson & Johnson Vision, Santa Ana, CA). METHODS: In this retrospective cohort analysis, patients undergoing femtosecond laser-assisted cataract surgery (FLACS) or manual cataract surgery between June and September 2018 from a single surgical center in Toronto, Canada, were considered for inclusion. Postoperative corneal astigmatism values were compared to preoperative astigmatism indices to determine the SIA at the postoperative 3-month (POM3) mark using the Alpins vector method. Secondary outcomes included postoperative corrected distance visual acuity (CDVA), central corneal thickness (CCT), and CCI morphology parameters. RESULTS: Refractive outcomes from 104 eyes of 61 patients (54 eyes in the manual group and 50 eyes in the FLACS group) were included. There was no significant difference in POM3 SIA (manual: 0.45 ± 0.28 diopters [D], FLACS: 0.57 ± 0.46 D, P = .11); however, a significantly larger SIA was noted in the FLACS cohort at postoperative 1 week (P = .02) and 1 month (P = .04). FLACS led to a significantly smaller POM3 CCI thickness (P = .006), and CCI position was comparable between the two techniques (P = .44). There were no significant differences between groups in CDVA (P = .19), CCT thickness (P = .20), or phacoemulsification time (P = .59). CONCLUSIONS: There was no significant difference in SIA between FLACS and manual cataract surgery at POM3. With a significantly smaller CCI thickness seen in FLACS cases, and a comparable CCI position, the reason for the increased SIA following laser CCIs in the early postoperative period was unclear. [J Refract Surg. 2019;35(12):796-802.].
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".