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Record W2773219283 · doi:10.1016/j.artres.2017.10.046

4.8 ARTERIAL STIFFNESS AND ITS RELATIONSHIP TO MORTALITY IN PATIENTS WITH PERIPHERAL ARTERY DISEASE

2017· article· en· W2773219283 on OpenAlexaff
Gabriel Dimitrov, Giovanni Scandale, Martino Recchia, Edoardo Perilli, Marzio Minola, Gianni Carzaniga, Maria Carotta

Bibliographic record

VenueArtery Research · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMedicineArterial diseasePeripheralArterial stiffnessCardiologyInternal medicineDiseaseVascular diseaseBlood pressure

Abstract

fetched live from OpenAlex

Several studies (1,2) suggest that patients with peripheral artery disease (PAD) show an increase in arterial stiffness, nevertheless the impact on mortality is less documented. (3) 228 PAD patients mean age (68 ± 9 years) were followed-up for 4,8 ± 2 years. Anthropometric and clinical measurements were collected, ankle-brachial index (ABI) was estimated with standard protocol and hemodynamic parameters (central blood pressure, aortic pulse wave velocity [aPWV], augmentation index [Aix]) were measured using applanation tonometry. Prognostic factors of mortality were identified by Cox proportional hazards regression model. During follow-up 26 (11,6%) deaths occurred. Among them, 5 (19%) were of cardiovascular origin. The Cox analysis applied to data relative to the third tertile of aPWV (11.4–21.4, m/s), is significant for age, (p = 0.039), smoking history (p = 0.0003) non use of lipid lowering drugs (p = 0.026) and lower height (p = 0.007) but not for aPWV (p = 0.312), Aix (p = 0.075) and ABI (p = 0.305). The present study provides further insights into the lack of association between large artery stiffness, pressure wave reflections and mortality in PAD patients.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.089
GPT teacher head0.380
Teacher spread0.291 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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