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Record W2103951129 · doi:10.3810/psm.2012.05.1964

Exercise Interventions for Patients with Peripheral Arterial Disease: A Review of the Literature

2012· review· en· W2103951129 on OpenAlexaff
Ingrid Brenner, Monica Parry, Cameron Brown

Bibliographic record

VenueThe Physician and Sportsmedicine · 2012
Typereview
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsQueen's UniversityUniversity of TorontoTrent University
Fundersnot available
KeywordsArterial diseaseMedicinePsychological interventionPeripheralPhysical therapyDiseasePhysical medicine and rehabilitationIntensive care medicineVascular diseaseInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Peripheral arterial disease (PAD) is a common chronic cardiovascular condition that affects the lower extremities and can substantially limit daily activities and quality of life. Lifestyle interventions, including smoking cessation, diet modification, regular physical activity, and pharmacotherapy, are often prescribed to treat patients with PAD. Exercise interventions can be effective in increasing claudication onset time and maximal walking distance. Of the various types of exercise interventions available for patients with PAD, little is known about the differences that may exist between men and women in patient response to such interventions. The purpose of this literature review is to examine the current knowledge of exercise interventions for individuals with mild (Fontaine stages I-II) PAD and to consider any differences that may exist between men and women. Women with PAD present with a different clinical profile compared with men, but respond similarly to an acute bout of exercise and a training program. Patients with PAD should be encouraged to walk regularly; however, more research is needed to determine differences between men and women in their response to various exercise interventions.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.651
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.022
GPT teacher head0.305
Teacher spread0.283 · 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 designSystematic review
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

Citations25
Published2012
Admission routes1
Has abstractyes

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