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Record W4401149890 · doi:10.22374/jclrs.v8i1.61

OPPORTUNITIES FOR IMPROVING THE LONG-TERM MANAGEMENT OF KERATOCONUS PATIENTS

2024· article· en· W4401149890 on OpenAlexvenueno aff
Amy Nau, Cherie B. Nau, Ellen Shorter, Muriel Schornack, Jennifer Swingle Fogt, Jennifer Harthan

Bibliographic record

VenueJournal of Contact lens Research and Science · 2024
Typearticle
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsnot available
FundersNational Center for Advancing Translational Sciences
KeywordsKeratoconusTerm (time)MedicineIntensive care medicineComputer scienceOphthalmologyPhysicsCornea

Abstract

fetched live from OpenAlex

Background and Objective: This study determined whether practitioners specializing in keratoconus (KC) adhere to published guidelines for disease management and to what extent comorbid conditions of dry eye, contact lens tolerance, and psychological consequences of KC are formally assessed as part of long-term management. Materials and Methods: This cross-sectional study used an IRB-approved, Internet-based, REDCap platform. Descriptive statistics are presented. Results: A total of 222 participants qualified for participation. Most 134 (60%) followed young and unstable patients every 6 months and less frequent follow-up examinations for patients with stable findings, with 142 (64%) recommending annual examinations. Scleral lenses were the preferred optical correction method (36%), followed by corneal gas permeable lenses (21%). A total of 118 (55%, n=216) participants recommend crosslinking to any patient with documented disease progression regardless of age. Fewer than 25% of patients were referred for surgical correction of KC. Half of respondents, 114 (51%), reported testing for tear film dysfunction, while 108 (49%) never tested. No participants used a depression screening instrument. Conclusion: Practitioners managing patients with KC largely adhere to current consensus recommendations. This survey identified several potentially high-impact, low-cost improvements to current practice patterns, including screening for dry eye and depression.

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)0.000
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 score0.743
Threshold uncertainty score0.156

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.124
GPT teacher head0.382
Teacher spread0.258 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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