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Limits of Agreement Between the Optical Pachymeter and a Noncontact Specular Microscope

2005· article· en· W2002257978 on OpenAlexaff
Kelechi C. Ogbuehi, Turki Almubrad

Bibliographic record

VenueCornea · 2005
Typearticle
Languageen
FieldMedicine
TopicCorneal surgery and disorders
Canadian institutionsCegep de Sept Iles
Fundersnot available
KeywordsMicroscopeSpecular reflectionOptical microscopeOpticsSlit lampMaterials scienceLimits of agreementNuclear medicineMedicinePhysicsScanning electron microscope

Abstract

fetched live from OpenAlex

PURPOSE: To determine the limits of agreement between central corneal thickness (CCT) measurements made with the slit lamp-attached optical pachymeter and the SP2000P noncontact specular microscope. METHODS: Triplicate readings for CCT were obtained for each of 130 (right) eyes of 130 patients, using the slit lamp-attached optical pachymeter and then the SP2000P noncontact specular microscope. The average CCT measured by each method was compared. Subsequently, the mean difference between both sets of measurements was assessed, and the 95% confidence interval (limits of agreement) between both techniques was determined. RESULTS: The mean +/- SD CCT measured by the optical pachymeter was 543 +/- 34 microm and 532 +/- 34 microm for the specular microscope. We found a statistically significant (P < 0.001) mean bias of 10 mum between CCT values measured with both types of equipment, with the optical pachymeter returning the higher values. The coefficient of variation was 6.3% for the optical pachymeter and 6.4% for the specular microscope. CONCLUSIONS: The right eye CCT measurements made by the optical pachymeter are, on average, 10 mum thicker than those made with the SP2000P specular microscope, which suggests that both pieces of equipment cannot be used interchangeably to monitor CCT changes in patients. Excluding left eye measurements, the reliability of the optical pachymeter is identical to that of the noncontact specular microscope.

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 score0.319
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.028
GPT teacher head0.280
Teacher spread0.252 · 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

Citations8
Published2005
Admission routes1
Has abstractyes

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