Femtosecond laser versus manual dissection for top hat penetrating keratoplasty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it