Incidence and risk factors for pressure injuries in patients who have undergone vascular operations: a scoping review
Bibliographic record
Abstract
BACKGROUND: Patients who have undergone vascular operations are thought to be at an increased risk for developing pressure injuries; however, the extent to which pressure injuries occur in this population is not clear. This scoping review sought to summarize what is known about the incidence of pressure injuries, and the risk factors for the development of pressure injuries in patients who have undergone vascular operations. MAIN: An initial search identified 2564 articles, and 9 English language studies were included. Results showed that due to study design limitations in the available literature preventing hospital-acquired and present on admission pressure injuries to be distinguished, it is difficult to ascertain the incidence rate of pressure injuries in this population. CONCLUSION: Certain vascular procedures were found to be higher risk for the development of pressure injuries such as major amputations and lower extremity bypass surgery. In addition to procedural risk factors, patient factors were identified that may be associated with the development of pressure injuries in the vascular population, and these in the authors' view deserve further exploration. Overall, this scoping review identified an area ripe for future research, the results of which would have implications for wound care in healthcare institutions and at home.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".