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Record W1984908741 · doi:10.1097/icl.0b013e318157c98f

New Technologies to Assess Lens-Mediated Effects of the Cornea

2007· review· en· W1984908741 on OpenAlexaff
Desmond Fonn, Trefford Simpson, Jill Woods, Craig A. Woods

Bibliographic record

VenueEye & Contact Lens Science & Clinical Practice · 2007
Typereview
Languageen
FieldMedicine
TopicCorneal surgery and disorders
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCorneaOptical coherence tomographyLens (geology)Effects of long-term contact lens wear on the corneaContact lensConfocal microscopyOpticsCorneal epitheliumOphthalmologyMedicineMaterials scienceBiomedical engineeringPhysics

Abstract

fetched live from OpenAlex

Contact lenses can affect the cornea in a variety of ways. Corneal structure can be altered so that its thickness changes to involve the epithelium and the stroma. As a result, the curvature may be affected, but whether it is the front or the back surface that is affected depends on the type of lens used. If thickness increases sufficiently, corneal transparency may decrease. Contact lenses can also affect cellular structure of all layers of the cornea through mechanical trauma, hypoxia, or toxicity from solutions that are used in association with lenses. More serious complications, such as inflammation and infection, can arise. All these changes can be detected by clinicians using slitlamp biomicroscopes and keratometers if the changes are significant enough. Since the development of computers, optical instruments have become more sophisticated and have enabled the detection of subtle changes but have also facilitated more precise measurement of these conditions along with the ability to capture images of the alterations or defects. This article describes some of the newer techniques and, specifically, the application of optical coherence tomography, confocal microscopy, and esthesiometry.

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)0.008
metaresearch head score (Gemma)0.206
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.206
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.004
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.003
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 head0.199
GPT teacher head0.491
Teacher spread0.292 · 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.

Study designOther design
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
Published2007
Admission routes1
Has abstractyes

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