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Record W3136555611 · doi:10.1016/j.cjco.2021.03.005

Medical Management of Peripheral Arterial Disease: Deciphering the Intricacies of Therapeutic Options

2021· review· en· W3136555611 on OpenAlexafffund
Sanjot S. Sunner, Robert C. Welsh, Kevin R. Bainey

Bibliographic record

VenueCJC Open · 2021
Typereview
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsCanadian VIGOUR CentreUniversity of Alberta
FundersAbbott VascularEli Lilly and CompanyUniversity of AlbertaAstraZenecaBayerHeart and Stroke Foundation of CanadaBoehringer Ingelheim
KeywordsMedicineArterial diseaseCoronary artery diseaseGuidelineDiseaseGynecologyIntensive care medicineVascular diseaseInternal medicinePathology

Abstract

fetched live from OpenAlex

Due to the pathophysiology of atherosclerosis, the management for coronary artery disease and peripheral arterial disease (PAD) were considered homogenous, with therapies focused on the use of lipid-lowering medications, antiplatelet therapy, glucose control, and blood pressure management. However, more recently, studies have supported the use of tailored therapeutics and medical targets for patients with PAD that sometimes differ from those for coronary artery disease. Moreover, we are now witnessing large randomized PAD-specific trials that have altered therapeutic regimens and targets. Given these updates, dissemination of knowledge is lacking, as evidenced by discordant guideline recommendations. This comprehensive review provides an overview of contemporary therapeutic options for secondary prevention for patients with PAD.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.063
GPT teacher head0.383
Teacher spread0.320 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations10
Published2021
Admission routes2
Has abstractyes

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