Characteristics and recurrence of pterygium in Saudi Arabia: a single center study with a long follow‐up
Bibliographic record
Abstract
BACKGROUND: This study described the clinical features of patients with pterygium and analyzed the recurrence rate of conjunctival autografting alone, conjunctival autografting combined with intraoperative mitomycin C, and amniotic membrane grafting. METHODS: A retrospective cohort study of primary pterygium was conducted between January 2017 and February 2020. Factors associated with pterygium severity and recurrence were analyzed by univariate analysis and logistic regression models. RESULTS: The study included 292 patients with an average age of 53.3 ± 14.1 years, while the number of operated cases was 94. Pterygia involving the cornea were observed in 55 % of the cases. The overall rate of recurrence for the three procedures was 17 %. The average time of recurrence was 14.2 ± 11.9 months, with 37 % of the recurrences occurring after the first year. The only factor associated with a significant risk of recurrence was dry eye disease in both univariate (p = 0.021) and multivariate analysis (p = 0.026). The recurrence rates following conjunctival autografting with and without mitomycin C were 15.6 and 15.8 %, respectively. The recurrence rate following the amniotic membrane graft was twofold (OR= 2.02) (27 %) that following the conjunctival autograft (15.8 %). CONCLUSIONS: The only factor associated with the recurrence of pterygium was dry eye disease. More than one-third of recurrences developed after the first year, which stresses the importance of a long follow-up. The recurrence rate in our study following conjunctival graft was slightly higher compared to the literature mainly due to differences in study areas, populations, and follow-up periods.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".