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.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".