Posterior Corneal Surface Changes After Pterygium Excision Surgery
Bibliographic record
Abstract
PURPOSE: To evaluate the effect of pterygium excision on the posterior corneal surface and analyze the factors associated with those changes. METHODS: A prospective, interventional study including 33 eyes of 31 patients who underwent pterygium excision at the Tel Aviv Medical Center (Tel Aviv, Israel). Exclusion criteria included corneal dystrophy, pseudopterygium, corneal scarring, or previous ocular surgery in the treated eye. Data were obtained by using the Galilei dual Scheimpflug analyzer. Recorded posterior corneal data included steep keratometry, flat keratometry, mean keratometry, corneal astigmatism, best-fit sphere, and the squared eccentricity index (e). Posterior surgically induced astigmatism (SIA) was calculated to demonstrate the astigmatic effect of surgery. Anterior-segment high resolution optical coherence tomography was used to measure pterygium dimensions (depth and horizontal/vertical size). RESULTS: The mean age was 53.7 ± 16.7 years. Posterior corneal SIA was 0.9 ± 1.1 D (P < 0.001) and was significantly correlated with age (r = 0.568, P = 0.002), horizontal pterygium size (r = 0.387, P = 0.046), and preoperative posterior astigmatism (r = 0.688, P < 0.001). In a multivariable analysis, only age (coefficient = 0.010, P = 0.038) and preoperative posterior astigmatism (coefficient = 0.648, P = 0.002) remained significant. Pterygium dimensions were not significantly associated with SIA magnitude. Flat keratometry steepened by 0.5 ± 1.1 D (P = 0.019), mean keratometry steepened by 0.3 ±0.6 D (P = 0.035), posterior astigmatism was reduced by 0.4 ± 1.2 D (P = 0.072), and e decreased by 5.1 ± 17.3 (P = 0.021). CONCLUSIONS: Pterygium excision has a significant astigmatic effect on the posterior corneal surface. The astigmatic effect increases with age and with higher preoperative posterior astigmatism. Pterygium depth and size are not associated with the degree of surgical astigmatic effect.
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 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) | 0.001 | 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 | 0.001 |
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; both teacher heads agree on what is shown here.
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".