MétaCan
Menu
Back to cohort
Record W2027913771 · doi:10.1117/1.3425654

Virtual four-dimensional imaging of lung parenchyma by optical coherence tomography in mice

2010· article· en· W2027913771 on OpenAlex
Sven Meißner, Arata Tabuchi, Michael Mertens, Wolfgang M. Kuebler, Edmund Koch

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Biomedical Optics · 2010
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsSt. Michael's Hospital
FundersDeutsche Forschungsgemeinschaft
KeywordsLungOptical coherence tomographyParenchymaMedicineVentilation (architecture)PathologyTomographyX-ray microtomographyNuclear medicineBiomedical engineeringRadiologyInternal medicinePhysics

Abstract

fetched live from OpenAlex

In this feasibility study, we present a method for virtual 4-D imaging of healthy and injured subpleural lung tissue in the ventilated mouse. We use triggered swept source optical coherence tomography (OCT) with an A-scan frequency of 20 kHz to image murine subpleural alveoli during the inspiratory phase. The data acquisition is gated to the ventilation pressure to take single B-scans in each respiration cycle for different pressure levels. The acquired B-scans are combined off-line into one volume scan for each pressure level. The air fraction in healthy lungs and injured lungs is measured using 2-D OCT en-face images. Upon lung inspiration from 2 to 12 cm H(2)O ventilation pressure, the air fraction increases in healthy lungs by up to 11% and in injured lungs by 8%. This expansion correlates well with results of previous studies, reporting increased alveolar area with increased ventilation pressures. We demonstrate that OCT is a useful tool to investigate alveolar dynamics in spatial dimensions.

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.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.807
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.231
Teacher spread0.225 · 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