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Record W2338419978 · doi:10.2147/opth.s80102

The use of dry amniotic membrane in pterygium surgery

2016· review· en· W2338419978 on OpenAlexaff
Gelareh Noureddin, Sonia N. Yeung

Bibliographic record

VenueClinical ophthalmology · 2016
Typereview
Languageen
FieldMedicine
TopicCorneal Surgery and Treatments
Canadian institutionsVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicinePterygiumOphthalmology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.968
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.457
GPT teacher head0.483
Teacher spread0.026 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations26
Published2016
Admission routes1
Has abstractyes

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