Diabetes and carotid artery disease: a narrative review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Diabetes mellitus (DM) has been linked to an increased prevalence and severity of carotid artery disease, as well as polyvascular disease. Carotid disease is also associated with obesity and abnormal peri-organ and intra-organ fat (APIFat) deposition (i.e., excess fat accumulation in several organs such as the liver, heart and vessels). In turn, DM is associated with APIFat. The coexistence of these comorbidities confers a greater risk of vascular events. Clinicians should also consider that carotid bruits may predict cardiovascular risk. DM has been related to a greater risk of adverse outcomes after carotid endarterectomy or stenting. Whether modifying risk factors (e.g., glycaemia and dyslipidaemia) in DM patients can improve the outcomes of these procedures needs to be established. Furthermore, DM is a risk factor for contrast-induced acute kidney injury (CI-AKI). The latter should be recorded in DM patients undergoing carotid stenting since it can influence both short- and long-term outcomes. From a pathophysiological perspective, functional changes in the carotid artery may precede morphological ones. Furthermore, carotid plaque characteristics are increasingly being studied in terms of vascular risk stratification and monitoring short-term changes attributed to treatment. The present narrative review discusses the recent (2019) literature on the associations between DM and carotid artery disease. Physicians and vascular surgeons looking after patients with carotid disease and DM should consider these links that may influence outcomes. Further research in this field is also needed to optimise the treatment of such 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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it