Zig Zag versus Top Hat configuration in IntraLase-enabled penetrating keratoplasty
Bibliographic record
Abstract
AIM: To compare the outcomes with IntraLase-enabled keratoplasty using (IEK) Top Hat (TH) versus Zig Zag (ZZ) configuration. METHODS: Retrospective comparative series of 24 eyes that underwent TH and 10 eyes that underwent ZZ IEK. RESULTS: There were no significant differences in LogMar Best-spectacle corrected visual acuity (TH- IEK=0.3; ZZ-IEK=0.18, p=0.18), spherical equivalent (TH-IEK=-3.55±3.7 dioptres (D); ZZ-IEK=-2.69±4.85 D, p=0.60), manifest cylinder (TH- IEK=3.79±2.43 D; ZZ- IEK=4.61±3.29 D, p=0.45), topographic astigmatism (TH-IEK=3.67±2.34 D; ZZ-IEK=4.26±1.1 D, p=0.63), total higher-order aberrations (TH- IEK=8.26±3.53; ZZ-IEK=8.1±4.71, P=0.92), endothelial cell density change from baseline (TH- IEK= -41.55%±15.86; ZZ-IEK=-25.45%±30.66, p=0.22) or time to suture removal in months (TH- IEK=7.48±4.07; ZZ- IEK=6.93±2.71, p=0.75). There was no difference in requirements for astigmatic keratectomy (TH-IEK=54.2%±13; ZZ-IEK=50%±5, OR=1.18) or complications (TH-IEK=25%±6; ZZ-IEK=30%±3, OR=0.78). CONCLUSIONS: TH-IEK and ZZ-IEK have comparable visual and refractive outcomes, wound healing and endothelial cell counts at 1-year.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.004 | 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 teacher head, 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".