Endothelial dysfunction assessment by noninvasive peripheral arterial tonometry in patients with chronic obstructive pulmonary disease compared with healthy subjects
Bibliographic record
Abstract
INTRODUCTION: Patients with chronic obstructive pulmonary disease (COPD) have an increased risk of cardiovascular disease. The endothelial dysfunction likely plays a central role in increasing cardiovascular risk. OBJECTIVES: This cross-sectional, study investigated the prevalence and extent of endothelial dysfunction in patients with stable COPD. METHODS: Peripheral arterial tonometry (PAT) was measured by post-ischemic reactive hyperemia index (RHI) in 16 COPD patients, 16 healthy controls and 16 subjects with treated systemic arterial hypertension (AH) and analysed with covariates condition (dyslipidemia, and medications). RESULTS: and RHI were directly correlated (Spearman index = 0.553; P = .026). COPD patients in groups C and D according to Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages showed lower RHI compared with patients classified as A and B (P < .01). At multiple regression analysis the presence of dyslipidemia, COPD and AH were associated with the presence of endothelial dysfunction. CONCLUSIONS: Endothelial dysfunction in stable COPD patients is probably implicated in the high cardiovascular comorbidity. This study suggests the potential utility of endothelial dysfunction evaluation in patients with COPD to a timely assessment and treatment for cardiac complications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".