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Record W2985154509 · doi:10.1016/j.jcrs.2019.06.027

Corneal crosslinking: Current protocols and clinical approach

2019· review· en· W2985154509 on OpenAlexaff
Kenneth A. Beckman, Preeya K. Gupta, Marjan Farid, John P. Berdahl, Elizabeth Yeu, Brandon D. Ayres, Clara C. Chan, José Álvaro Pereira Gomes, Edward J. Holland, Terry Kim, Christopher E. Starr, Francis S. Mah

Bibliographic record

VenueJournal of Cataract & Refractive Surgery · 2019
Typereview
Languageen
FieldMedicine
TopicCorneal surgery and disorders
Canadian institutionsBank of CanadaUniversity of Toronto
FundersAllerganJohnson and JohnsonConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo à Pesquisa do Estado de São PauloNovartis Pharmaceuticals Corporation
KeywordsKeratoconusCorneal transplantationMedicineCorneaCorneal diseaseCorneal epitheliumTransplantationUltraviolet lightOphthalmologyOptometrySurgeryChemistry

Abstract

fetched live from OpenAlex

Members of the ASCRS Cornea Clinical Committee performed a review of the current literature on the corneal crosslinking (CXL) procedure for treating corneal ectasia. The members explored the data on the techniques currently in use and under investigation, including their advantages, safety profiles, risks, and cost analyses, compared with data on corneal transplantation. They concluded that CXL limits the progression of keratoconus, thus reducing the need for transplantation. They also found that compared with permitting the disease to progress naturally, CXL techniques carry significant and long-term cost and safety benefits, primarily by reducing the need for corneal transplantation. Studies of various CXL techniques (eg, epithelium-on treatment, changes in ultraviolet light parameters, riboflavin composition) continue with the ultimate goal of improving the procedure's safety and efficacy.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.005

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 head0.296
GPT teacher head0.502
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), 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

Citations63
Published2019
Admission routes1
Has abstractyes

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