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Record W4290739510 · doi:10.3389/fphot.2022.938144

Dynamic vascular optical spectroscopy for monitoring peripheral arterial disease patients undergoing a surgical intervention

2022· article· en· W4290739510 on OpenAlexfundno aff
Alessandro Marone, Nisha Maheshwari, S. K. Kim, Danielle Bajakian, Andreas H. Hielscher

Bibliographic record

VenueFrontiers in Photonics · 2022
Typearticle
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteYork University
KeywordsMedicinePeripheralPerfusionRevascularizationAngiographyRadiologyArterial diseaseIntervention (counseling)Vascular diseaseSurgeryCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Peripheral arterial disease (PAD) patients experience a reduction in blood supply to the extremities caused by an accumulation of plaque in their arterial system. In advanced stages of PAD, surgical intervention is often required to reopen arteries and restore limb perfusion to avoid necrosis and amputations. To determine the success of an intervention, it is necessary to confirm that reperfusion was achieved after the intervention in areas of the foot that lacked perfusion before the intervention. The standard procedure to obtain this information is to perform repeated X-ray angiography. However, this approach requires a relatively high radiation dose and the extensive use of contrast agents. To overcome these issues, our lab has developed a system that uses dynamic vascular optical spectroscopy (DVOS) to monitor perfusion in the foot in real-time before, during, and after an intervention. In the explorative study presented in this paper, we monitored ten patients undergoing revascularization surgery. We found that there is a clear change in the DVOS signal in cases when reperfusion to affected areas in the foot is established. It was also possible to assess the effects that balloon inflations and deflations and contrast agent injections had on the downstream vasculature of the patients.

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)0.000
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 score0.513
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.257
Teacher spread0.251 · 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 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

Citations3
Published2022
Admission routes1
Has abstractyes

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