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Validity of Pachymetric Measurements by Manipulating the Acoustic Factor of Orbscan II

2006· article· en· W2011718472 on OpenAlexaff
Fenghe Lu, Trefford Simpson, Desmond Fonn, Luigina Sorbara, Lyndon Jones

Bibliographic record

VenueEye & Contact Lens Science & Clinical Practice · 2006
Typearticle
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPsychologyAudiologyMedicine

Abstract

fetched live from OpenAlex

PURPOSE: To assess the validity of pachymetric measurements by examining the constancy of the acoustic factor (AF) of the Orbscan II (Orbtek, Bausch & Lomb, Rochester, NY) after overnight rigid gas-permeable (RGP) contact lens wear. METHODS: Twenty participants wore CRT (Paragon Vision Sciences, Mesa, AZ) HDS 100 contact lenses on one eye and control lenses on the contralateral eye for one night while sleeping. Another 24 participants wore CRT lenses on both eyes for one night. Central corneal thickness was measured using optical coherence tomography and Orbscan II on the night before lens use, immediately after lens removal on the following morning, and 1, 3, 6, and 12 hours later. By using optical coherence tomography as a reference, the adjusted AF was calculated by using a least squares method over time. RESULTS: The adjusted AF depended on the corneal thickness in normally hydrated corneas. The adjusted AF and the percentage change of the adjusted AF varied before and after overnight lens wear. There was a strong and significant correlation between the corneal swelling and the percentage change of the adjusted AF (all r at least 0.91, P<0.05). CONCLUSIONS: The adjusted AF is a variable, not a constant. The AF is a function of the corneal thickness and its alteration with, for example, corneal swelling. The validity of the adjusted Orbscan II pachymetric measures using a single AF is untenable.

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)0.004
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.139
GPT teacher head0.408
Teacher spread0.270 · 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 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

Citations12
Published2006
Admission routes1
Has abstractyes

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