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Record W2181138415 · doi:10.1364/boe.6.004365

Reproducibility of optical coherence tomography airway imaging

2015· article· en· W2181138415 on OpenAlexafffund
Miranda Kirby, Keishi Ohtani, Taylor Nickens, Rosa María López Lisbona, Anthony M. D. Lee, Tawimas Shaipanich, Pierre Lane, Calum MacAulay, Stephen Lam, Harvey O. Coxson

Bibliographic record

VenueBiomedical Optics Express · 2015
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsBC Cancer AgencySt. Paul's Hospital
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BCBritish Columbia Lung Association
KeywordsReproducibilityOptical coherence tomographyAirwayMedicineCoefficient of variationBiomedical engineeringPreclinical imagingNuclear medicineTomographyRadiologyIn vivoSurgeryBiologyChemistry

Abstract

fetched live from OpenAlex

Optical coherence tomography (OCT) is a promising imaging technique to evaluate small airway remodeling. However, the short-term insertion-reinsertion reproducibility of OCT for evaluating the same bronchial pathway has yet to be established. We evaluated 74 OCT data sets from 38 current or former smokers twice within a single imaging session. Although the overall insertion-reinsertion airway wall thickness (WT) measurement coefficient of variation (CV) was moderate at 12%, much of the variability between repeat imaging was attributed to the observer; CV for repeated measurements of the same airway (intra-observer CV) was 9%. Therefore, reproducibility may be improved by introduction of automated analysis approaches suggesting that OCT has potential to be an in-vivo method for evaluating airway remodeling in future longitudinal and intervention studies.

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)0.020
metaresearch head score (Gemma)0.041
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 score0.020
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.041
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.022
GPT teacher head0.261
Teacher spread0.239 · 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

Citations24
Published2015
Admission routes2
Has abstractyes

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