Extensive versus Limited Pterygium Excision with Conjunctival Autograft: Outcomes and Recurrence Rates
Bibliographic record
Abstract
PURPOSE: To compare the long-term outcomes and recurrence rate of extensive versus limited subconjunctival pterygium excision with conjunctival autograft. METHODS: This retrospective study included 135 consecutive patients (161 eyes) who had pterygium excision with conjunctival autograft at the cornea performed at the cornea service of the Toronto Western Hospital. Ninety-one had limited pterygium excision, and 70 had extensive pterygium excision with conjunctival sparing. Main outcome measures included the recurrence rate. RESULTS: The recurrence rate in the limited excision group was 12.1% compared with only 4.3% in the extensive pterygium excision group (p = 0.14). The mean time to recurrence was shorter with limited excision compared to extensive excision (4.0 vs. 5.3 months, respectively, p = 0.16). Limited pterygium excision had a hazard ratio of recurrence of 3.2 compared with the extensive excision method. Recurrence-free survival analysis showed a significant advantage for the extensive excision group (p = 0.045, log-rank test). Cox proportional hazards regression found that younger age (p = 0.0003), larger area of corneal involvement (p = 0.004), worse preoperative visual acuity (p = 0.01), and limited pterygium excision (p = 0.04) significantly increased the risk for recurrence. CONCLUSIONS: Both limited and extensive pterygium excision groups had low recurrence rates. The extensive subconjunctival pterygium excision group tended toward fewer recurrences, which occurred later.
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 | 0.002 |
| 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 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".