CORRELATION OF THE BRADEN SCALE AND COMORBIDITIES WITH PRESSURE INJURY PREVALENCE IN A GERIATRIC HOSPITAL
Bibliographic record
Abstract
Background: Pressure injuries are common issue in all hospital settings with Canadian prevalence rates up to 26%. As these injuries can lead to pain, severe infection, loss of function and even death, prevention is desirable. Pressure injuries are staged 1–4, deep tissue injury and unstageable. At our non-acute geriatric hospital, a quality improvement project has been initiated to attempt to reduce pressure injuries of all stages. The aim of our study is to assess whether demographics, Braden scale scores and co-morbidities are associated with the presence of all stages of pressure injuries. Methods: 216 patients in a non-acute geriatric hospital who consented were assessed on one day for presence of pressure injury. Prevalence ratios (PR) were estimated using robust standard errors after chart review. Results: Neurological and renal comorbidities were associated with presence of pressure injury, univariately. Only renal-related comorbidity remained significant (PR=1.9 95%CI 1.3–2.7) after adjustment for Braden scores (reference, no risk of pressure injuries, moderate risk PR=5.5 95%CI 2.5–12.1 and high risk PR=6.3 95%CI 2.9–13.8). Conclusion: Braden scale as well as certain co-morbidities such as neurological and renal comorbidities could be integrated into care plans and be used to help identify geriatric patients at risk of developing pressure injuries. As our patient numbers are small, it would be reasonable to repeat this study at other similar geriatric institutions looking at Braden scale and comorbidities to have more geriatric patients enrolled and to compare their results.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".