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Record W4406955184 · doi:10.5935/0004-2749.2023-0332

Analysis and optimization of the landing zone parameters of a sclera lens model

2025· article· en· W4406955184 on OpenAlexaboutno aff
Luiz Formentin, Yandely Chihuantito Choquechambi, Natalia Pereira Felix de Araujo

Bibliographic record

VenueArquivos Brasileiros de Oftalmologia · 2025
Typearticle
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsnot available
Fundersnot available
KeywordsScleraOphthalmologyLens (geology)Scleral lensGeologyComputer scienceOptometryMedicineCornea

Abstract

fetched live from OpenAlex

PURPOSE: This study aimed to modify scleral contact lenses to achieve a desired compression standard and to evaluate the effectiveness and reliability of the adjustments. METHODS: In this nonrandomized, noncomparative, and partially masked study Scleral contact lens fittings were analyzed in 20 eyes of 12 patients (50% women, 50% men) diagnosed with keratoconus. Participants were selected based on their need for scleral contact lenses (SCLs), which was determined in complete ophthalmological examinations. Patients were tested with Zenlens scleral contact lenses (Bausch & Lomb, Vaughan, Ontario, Canada). We evaluated compression in the lens support area after one hour of use, excluding cases of peripheral lifting. Photos of the adaptations were sent to five experts for analysis of the quadrants (nasal, temporal, superior, and inferior). We used Fisher's exact test for statistical analysis. RESULTS: The proposed adjustment was highly effective (93.5% correct) in lens delivery (BL=0), with the interrater agreement between doctors ranging from 68.8% to 80.9%. CONCLUSION: The clinical parameters proposed for scleral contact lenses adjustment proved useful and reproducible, enabling their practical application to scleral lens adaptation.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.307

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.269
Teacher spread0.248 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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