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Record W2021343623 · doi:10.1088/0967-3334/29/9/003

Arterial flow measurements during reactive hyperemia using NIRS

2008· article· en· W2021343623 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.

Bibliographic record

VenuePhysiological Measurement · 2008
Typearticle
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsMontreal Heart Institute
Fundersnot available
KeywordsReactive hyperemiaReproducibilityForearmPlethysmographMedicinePerfusionOxygenationPeripheralBlood flowBiomedical engineeringCardiologyInternal medicineChemistrySurgery

Abstract

fetched live from OpenAlex

Non-invasive evaluation of peripheral perfusion may be useful in many contexts including clinical research. We validated a novel non-invasive spectroscopy technique to quantify forearm arterial inflow. This method, which is based on the measurement of tissular total hemoglobin variations after an ischemic period, was compared to strain gauge plethysmography (SGP). The technique uses near-infrared spectroscopy (NIRS) to determine the rate of change of forearm tissue oxygenation during reactive hyperemia. In this study, 13 subjects were simultaneously evaluated with NIRS and SGP. Nine baseline flow measurements were performed to assess the reproducibility of each method. Twenty-seven serial measurements were then made to evaluate flow variation during forearm reactive hyperemia. SGP and NIRS methods showed excellent reproducibility with the same intra-class correlation coefficients (0.98). In conclusion, the NIRS technique appears well suited for non-invasive evaluation of quantitative arterial forearm flow.

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 categoriesMeta-epidemiology (narrow)
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 score0.934
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.250
GPT teacher head0.296
Teacher spread0.047 · 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