The use of dry amniotic membrane in pterygium surgery
Bibliographic record
Abstract
Pterygium is a fibrovascular growth of the bulbar conjunctiva that crosses the limbus and extends over the peripheral cornea, in some cases resulting in significant visual morbidity. When treatment is indicated, surgery is necessary, and several management options exist. These include excision, conjunctival autografting, and the use of adjuvant therapies. This paper reviews the incidence and prevalence of pterygia and also describes the various techniques currently used to treat this condition. These management options are compared to the use of dry amniotic membrane grafting (AMG), specifically with regard to recurrence rates, time to recurrence, safety and tolerability, as well as patient factors including cosmesis and quality of life. AMG has been used in the treatment of ocular surface disease due to a variety of benefits, including its anti-inflammatory properties, as well as its ability to promote epithelial growth and suppress transforming growth factor-β signaling and fibroblast proliferation. However, rates of recurrence for AMG following pterygium excision still surpass other commonly used techniques, including conjunctival and limbal autografting. Nevertheless, there are circumstances in which AMG may be most beneficial to the patient, such as when preexisting conjunctival scarring is present, when the conjunctiva must be spared for future glaucoma filtering surgery, or in cases of large or double-headed pterygia. Therefore, surgeons should be prepared to offer this procedure as an option to their patients for the treatment of pterygia.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".