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Record W4366605983 · doi:10.1093/fampra/cmad048

Peripheral artery disease (PAD) in primary care—educational experiences for PAD primary care in England—a mixed-method study

2023· article· en· W4366605983 on OpenAlexfundno aff
Bernadeta Bridgwood, Rob Sayers

Bibliographic record

VenueFamily Practice · 2023
Typearticle
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsnot available
FundersDavies Charitable Foundation
KeywordsMedicinePrimary careDiseaseArterial diseaseFamily medicineExperiential learningNursingVascular diseaseInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Peripheral artery disease (PAD), the pathophysiologic narrowing of arterial blood vessels of the lower leg due to atherosclerosis, is a highly prevalent disease, with sharp increases in prevalence with age. Primary care is ideally located to identify and manage PAD. OBJECTIVES: This study aims to identify the educational experiences, opinions, and confidence of primary care clinicians (PCCs) regarding PAD. METHOD: This mixed-method study was conducted within primary care in England. An online survey was completed with follow-on semistructured interviews, between January and September 2021, with PCCs, namely GPs, practice nurses, and allied professionals (survey n = 874, interviews n = 50). RESULTS: PCCs report variation in PAD education received, where the content could not often be recalled. Patient-focussed experiential and self-directed learning, formed the largest method to gain PAD education. All PCCs recognized that they have an important role in recognizing PAD yet confidence in recognizing and diagnosing PAD was lacking. PCCs acknowledged that late or missed PAD diagnosis resulted in significant patient morbidity and mortality. Yet many did not recognize PAD as a common disease. CONCLUSION: As "specialist-generalists" with finite resources, education provided to primary care needs to be applicable for the multimorbid patient presentations often seen, utilizing resources available in primary care, with consideration to the time constraints endured.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.337
Teacher spread0.312 · 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.

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

Citations14
Published2023
Admission routes1
Has abstractyes

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