Predicting factors of pressure ulcers in older Thai stroke patients living in urban communities
Bibliographic record
Abstract
AIM AND OBJECTIVE: To describe the contributing factors for pressure ulcer development in older Thai stroke patients in an urban community. BACKGROUND: Pressure ulcers are a common problem signifying a source of morbidity and mortality in older stroke patients. In the past, most studies have focused on the physiological factors affecting pressure ulcer development. However, studies related to effect of stroke severity and psychosocial factors in community-based setting have not found. DESIGN: Cross-sectional survey design. METHODS: One hundred and sixty-eight older stroke patients from urban communities in three districts in metropolitan Thailand were recruited for this cross sectional study. Data were collected from the Skin Assessment Tool, the Braden scale, the Canadian Neurological Scale, the Social Support Questionnaire and the Thai Geriatric Depression Scale for each patient. The results were analysed using multiple logistic regression and the chi-square test. RESULTS: Eighty older stroke patients who developed pressure ulcers (47·6%). The participants were aged between 60-93 years. Multiple logistic regression analysis was applied to calculate the odds ratio. The significant predicting factors for pressure ulcers were activity, moisture, nutrition, friction and shearing and depression. Sensory perception, mobility, severity of stroke and social support were not found to be significant predictors. CONCLUSIONS: Physiological factors and depression were found to be significant predicting factors for pressure ulcer development in older stroke patients living in an urban community. RELEVANCE TO CLINICAL PRACTICE: To help prevent pressure ulcers in older stroke patients, the findings suggest that healthcare providers assess activity, moisture, nutrition, friction and shearing, as well as psychological assessment for depression. The results indicate that models for pressure ulcer prevention merit further investigation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".