Incidental ocular surface squamous neoplasia in pterygia: a systematic review and meta-analysis
Bibliographic record
Abstract
AIMS: Differentiating pterygium from ocular surface squamous neoplasia (OSSN) is important for guiding management. This meta-analysis evaluates the prevalence and risk factors for incidental OSSN in clinically diagnosed pterygia. METHODS: Ovid Embase, MEDLINE, Cochrane Library and Web of Science were systematically searched from January 2000 to February 2025. Included studies analysed ≥100 clinically diagnosed pterygia via histopathology. Random-effects meta-analysis assessed the prevalence of incidental OSSN among pterygia. Risk factors were evaluated using the Mantel-Haenszel and inverse variance methods, and meta-regression analysed the influence of publication year, geographic proximity to the equator, and country-level ultraviolet (UV) radiation exposure. RESULTS: =95.3%). Meta-regression revealed that lower OSSN prevalence was associated with greater distance from the equator (OR 0.49, 95% CI 0.28 to 0.83, p<0.01), while higher prevalence was associated with greater UV exposure (OR 2.20, 95% CI 1.17 to 4.14, p=0.01). Publication year had no effect (p=0.98). Age (p=0.18), sex (p=0.45) and lesion location (p=0.60-0.82) did not differ between incidental OSSN cases and benign pterygia. Incidental OSSN prevalence also did not differ between primary and recurrent pterygia (p=0.23). Regional analyses revealed variation in prevalence: Europe (0.29%), Asia (0.76%), North America (1.03%), Oceania (8.57%) and South America (14.97%). CONCLUSIONS: This meta-analysis, based on low- to very low-certainty evidence, identified a 1.32% pooled prevalence of incidental OSSN in clinically diagnosed pterygia, highlighting the potential influence of UV exposure and equatorial proximity. The overlap in demographic and lesion characteristics between benign pterygia and OSSN underscores diagnostic challenges.
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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.033 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".