Femtosecond laser versus manual dissection for top hat penetrating keratoplasty
Bibliographic record
Abstract
AIM: To compare the outcomes of IntraLase-enabled top hat penetrating keratoplasty (IEK) versus retrospective results of manual top hat penetrating keratoplasty (TH-PKP) and conventional PKP. PATIENTS/METHODS: This non-randomised prospective study included 94 eyes: 23 eyes underwent IEK, 36 TH-PKP and 35 conventional PKP. Preoperative and postoperative manifest refraction, uncorrected and best-spectacle corrected visual acuity (BSCVA), high-order ocular aberrations (HOA), endothelial cell counts and complications were analysed. RESULTS: At 12 months of follow-up, the mean log MAR BSCVA was 0.32 (SD 0.31) in the IEK group, 0.53 (0.36) in the TH PKP group (p = 0.03) and 0.39 (0.30) in the conventional PKP group (p = 0.4). The mean spherical equivalent was similar between the groups and was less than -2.2 dioptres. The mean cylinder was similar in the IEK and conventional PKP group (3.6 (1.9) dioptres and 4.1 (1.8) dioptres, respectively), and was significantly lower than the TH-PKP group (5.1 (3.2) dioptres, p = 0.04). The complications rate and high-order ocular aberrations were similar between the three groups studied. The mean endothelial cell loss was significantly lower at 12 months of follow-up in the IEK and the TH-PKP groups versus conventional PKP (32.4% and 22.3% vs 40.8%, respectively) (p = 0.05). The mean time to suture removal was 4.1 (1.2) months in the IEK group and 3.9 (1.5) months in the TH-PKP group versus 9.7 (1.1) months in the conventional PKP group (p<0.0001). CONCLUSIONS: IEK is a safe and stable procedure. It results in higher endothelial counts and faster suture removal in comparison with the conventional PKP, and has less astigmatism and better BSCVA in comparison with the manual TH-PKP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".