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Record W4212804367 · doi:10.1161/circresaha.121.320702

Sex Differences in Peripheral Artery Disease

2022· review· en· W4212804367 on OpenAlexaff
Maria Pabón, Susan Cheng, S. Elissa Altin, Sanjum S. Sethi, Michael D. Nelson, Kerrie L. Moreau, Naomi M. Hamburg, Connie N. Hess

Bibliographic record

VenueCirculation Research · 2022
Typereview
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsSt. Michael's Hospital
FundersNational Heart, Lung, and Blood InstituteNational Institute on AgingNational Center for Advancing Translational SciencesNational Institute on Alcohol Abuse and Alcoholism
KeywordsMedicineDiseaseEpidemiologySexual dimorphismVascular diseaseSex characteristicsArterial diseasePopulationPeripheralFemale sexInternal medicine

Abstract

fetched live from OpenAlex

Peripheral artery disease (PAD) is a prevalent condition that confers substantial morbidity and mortality and remains underdiagnosed as well as undertreated in the overall population. Although PAD prevalence is similar or higher in women compared with men, associations of traditional and nontraditional risk factors with PAD and clinical manifestations of PAD differ by sex and may contribute to delayed or lack of diagnosis in women. Such sex-based differences in the manifestation of PAD may arise from sexual dimorphism in the vascular substrate in health as well as sex variation in the responses to vascular stressors. Despite the availability of proven therapies for improving symptoms and reducing risk of ischemic cardiovascular and limb events among patients with diagnosed PAD, important sex differences in treatment and outcomes have been observed. We provide an overview of current knowledge regarding sex differences in the epidemiology, pathophysiology, clinical presentation, and management of 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)0.002
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.283
GPT teacher head0.444
Teacher spread0.162 · 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

Citations182
Published2022
Admission routes1
Has abstractyes

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