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Record W2977923190 · doi:10.1177/2045894019883613

Optimizing imaging of the rat pulmonary microvasculature by micro‐computed tomography

2019· article· en· W2977923190 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePulmonary Circulation · 2019
Typearticle
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsUniversity of OttawaOttawa Hospital
FundersCanadian Institutes of Health Research
KeywordsMedicineMicrocirculationLungPerfusionPulmonary vasculatureRadiologyTomographyPulmonary hypertensionPathologyComputed tomographyInternal medicine

Abstract

fetched live from OpenAlex

Micro-computed tomography (micro-CT) is used in pre-clinical research to generate high-resolution three-dimensional (3D) images of organs and tissues. When combined with intravascular contrast agents, micro-CT can provide 3D visualization and quantification of vascular networks in many different organs. However, the lungs present a particular challenge for contrast perfusion due to the complexity and fragile nature of the lung microcirculation. The protocol described here has been optimized to achieve consistent lung perfusion of the microvasculature to vessels < 20 microns in both normal and pulmonary arterial hypertension rats. High-resolution 3D micro-CT imaging can be used to better visualize changes in 3D architecture of the lung microcirculation in pulmonary vascular disease and to assess the impact of therapeutic strategies on microvascular structure in animal models of pulmonary arterial hypertension.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.232
Teacher spread0.224 · 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