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Record W2799464624 · doi:10.22374/jclrs.v2i1.17

Collagen Crosslinking for Keratoconus Can Change Scleral Shape

2018· article· en· W2799464624 on OpenAlexvenueno aff
Gregory DeNaeyer, Donald R. Sanders

Bibliographic record

VenueJournal of Contact lens Research and Science · 2018
Typearticle
Languageen
FieldMedicine
TopicCorneal surgery and disorders
Canadian institutionsnot available
Fundersnot available
KeywordsScleral lensKeratoconusOphthalmologyMaterials scienceAnatomyCorneaMedicine

Abstract

fetched live from OpenAlex

Collagen crosslinking (CXL) for keratoconus is known to decrease, halt or even partially reverse progression of keratoconus. We report on a case where a substantial effect on scleral shape was also demonstrated. This keratoconus patient, who was successfully wearing a scleral lens, underwent collagen crosslinking OS. Three months post crosslinking, the patient was unable to wear his previous scleral lens due to lens discomfort. Elevation mapping pre and post crosslinking, with a new corneal-scleral topography system, showed a decrease in size and change in shape of the cone, as well as a substantial change in the scleral elevation pattern at a 16mm chord diameter. The change in the scleral elevation pattern was best observed on scleral shape plots which graph the sagittal height (SAG) value on the Y-axis vs. meridian on the X-axis. The pre-crosslinking plot resembled a standard toric curve although the depression inferiorly at 330° was deeper than that superiorly by 250µ. The post-crosslinking plot changed substantially with the steep axis superiorly being markedly attenuated: the SAG superiorly (102°) decreased post-crosslinking by >300µ. Attempts to virtually fit the eye surface post-crosslinking with a standard posterior toric haptic demonstrated a good fit superiorly and a poor fit inferiorly. The post-crosslinking scleral toricity plot was used to obtain quantitative information to manufacture a custom design conforming to this specific eye. The resulting lens was comfortable, well centered and resulted in a BCVA of 20/30.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.659
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.277
GPT teacher head0.445
Teacher spread0.168 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations4
Published2018
Admission routes1
Has abstractyes

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