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Record W4402846019 · doi:10.31579/2692-9759/104

Peripheral Vascular Disease

2023· article· en· W4402846019 on OpenAlexfundno aff
Rehan Haider

Bibliographic record

VenueCardiology Research and Reports · 2023
Typearticle
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsnot available
FundersUniversity of KarachiUniversity of Calgary
KeywordsPeripheralMedicineDiseaseVascular diseasePathologyInternal medicine

Abstract

fetched live from OpenAlex

Peripheral vascular disease comprises diseases of the arteries and veins outside the thoracic region.: peripheral arterial disease (PAD), carotid artery disease (CAD), and aortic aneurysmatic disorder (AAA). Other rare manifestations of atherosclerotic disorders (e.g., renovascular high blood pressure, abdominal angina, and ischemia of the top extremity) were briefly noted. Special concerns in patients with diabetes are addressed in relevant sections; for instance, infection in an ischemic foot in an affected person with diabetes is described within the phase of critical limb ischemia. Atherosclerosis is the primary cause of peripheral arterial vascular ailments. It is vital to appreciate that the pathogenic mechanisms of clinical atherosclerosis are dual: chronic obstruction and biotic. The chronic obstructive mechanism is the primary purpose of lower limb ischemia, and in patients with diabetes, it is far more regularly preceded by a thrombotic occasion. An affected person with moderate clay diction abruptly studies significantly shortening of walking distance or surprising onset of rest ache. Alternatively, the seemingly wholesome character develops claudication. A coronary period heart attack or stroke in an affected person with claudication is also a thrombotic event in a patient with chronic obstructive disorder. In general, patients with diabetes greater frequently develop symptoms of atherosclerotic headaches, they do it at a younger age and it may be greater difficult to treat and feature greater headaches with treatment (in particular with invasive treatment.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.296

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.064
GPT teacher head0.371
Teacher spread0.307 · 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
Published2023
Admission routes1
Has abstractyes

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