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Record W2080737123 · doi:10.1136/bjo.2003.020321

The effect of image alignment on capillary blood flow measurement of the neuroretinal rim using the Heidelberg retina flowmeter

2004· article· en· W2080737123 on OpenAlexafffund
Mitra Sehi

Bibliographic record

VenueBritish Journal of Ophthalmology · 2004
Typearticle
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsUniversity of Waterloo
FundersGlaucoma Research Society of Canada
KeywordsRepeatabilityIntraclass correlationMedicineBlood flowReproducibilityRetinaOphthalmologyOptic nerveFlow measurementBiomedical engineeringAnatomyOpticsMathematicsPhysicsRadiology

Abstract

fetched live from OpenAlex

AIM: To examine the influence of image alignment on the repeatability of blood flow measurements of the optic nerve. METHODS: 10 normal subjects were examined. Heidelberg retina tomograph imaging was performed to establish best location and focus for the temporal neuroretinal rim. Two high quality Heidelberg retina flowmeter (HRF) images were acquired for three methods of alignment: central, nasal, and temporal. A 10 x 10 pixel measurement window was selected and exactly reproduced on all images. The interquartile pixel values were used to calculate capillary flow. ANOVA, intraclass correlation coefficients (ICC) and the coefficient of repeatability (CoR) were used for analysis. RESULTS: There was no difference between methods (p = 0.47) or between visits (p = 0.51). The ICCs were 0.83 for the central, 0.34 for the nasal, and 0.42 for the temporal alignment. The CoR was 31.5 for central (mean effect 235.1), 234.6 for nasal, and 256.7 for temporal alignment. CONCLUSION: Central alignment was the most repeatable method for the measurement of neuroretinal rim capillary blood flow using the HRF.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.258
Teacher spread0.244 · 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 designBench or experimental
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
Published2004
Admission routes2
Has abstractyes

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