Assessment of risk of peripheral vascular disease and vascular care capacity in low- and middle-income countries
Bibliographic record
Abstract
BACKGROUND: This study aimed to describe national peripheral vascular disease (PVD) risk and health burden, and vascular care capacity in Ghana. The gap between PVD burden and vascular care capacity in low- and middle-income countries was defined, and capacity improvement priorities were identified. METHODS: Data to estimate PVD risk factor burden were obtained from the World Health Organization Study on Global Ageing and Adult Health (SAGE), Ghana, and the Institute of Health Metrics and Evaluation Global Burden of Disease (IHME GBD) database. In addition, a novel nationwide assessment of vascular care capacity was performed, with 20 vascular care items assessed at 40 hospitals in Ghana. Factors contributing to specific item deficiency were described. RESULTS: From the SAGE database, there were 4305 respondents aged at least 50 years with data to estimate PVD risk. Of these, 57·4 per cent were at moderate to risk high of PVD with at least three risk factors; extrapolating nationally, the estimate was 1 654 557 people. Based on IHME GBD data, the estimated disability-adjusted life-years incurred from PVD increased fivefold from 1990 to 2010 (from 6·3 to 31·7 per 100 000 persons respectively). Vascular care capacity assessment demonstrated marked deficiencies in items for diagnosis, and in perioperative and vascular surgical care. Deficiencies were most often due to absence of equipment, lack of training and technology breakage. CONCLUSION: Risk factor reduction and management as well as optimization of current resources are paramount to avoid the large burden of PVD falling on healthcare systems in low- and middle-income countries. These countries are not well equipped to handle vascular surgical care, and rapid development of such capacity would be difficult and expensive.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".