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Record W2546584233 · doi:10.1080/17469899.2017.1256774

Refractive surgery in patients with ectasia

2016· article· en· W2546584233 on OpenAlexaff
Davin Johnson, Mona Harissi‐Dagher

Bibliographic record

VenueExpert Review of Ophthalmology · 2016
Typearticle
Languageen
FieldMedicine
TopicCorneal surgery and disorders
Canadian institutionsHôpital Notre-Dame
Fundersnot available
KeywordsMedicinePhotorefractive keratectomyKeratoconusEctasiaOphthalmologyVisual rehabilitationRefractive surgeryVisual acuityKeratomileusisCorneal topographyCorneal transplantationOptometryCorneaSurgery

Abstract

fetched live from OpenAlex

Introduction: Corneal ectasia is a group of related diseases characterized by progressive corneal steepening and thinning. It includes keratoconus, pellucid marginal degeneration (PMD), and post-laser ectasia. Traditionally, treatment of these conditions has involved visual rehabilitation with spectacles or contact lenses, with often eventual need for corneal transplantation.Areas covered: This review focuses on refractive surgery treatment options available for ectasia including collagen cross-linking (CXL), excimer laser, intrastromal corneal ring segments (ICRSs), and intraocular lenses (IOLs). A comprehensive literature review using OVID Medline and Embase was undertaken.Expert commentary: CXL has shown promising short- and long-term results in stabilizing patients with corneal ectasia. When combined with excimer laser using topography- or wavefront-guided photorefractive keratectomy (PRK), many patients are able to gain significant improvements in visual acuity and contact lens tolerance, which may delay or prevent altogether the need for corneal transplantation. Other treatments such as ICRSs, phakic IOLs, and TORIC IOLs may be beneficial in select cases for visual rehabilitation.

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)0.000
metaresearch head score (Gemma)0.004
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 score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.019
GPT teacher head0.312
Teacher spread0.293 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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