Factors Associated with Pressure Ulcer Risk in Spinal Cord Injury Rehabilitation
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to identify patient and clinical factors most strongly associated with a spinal cord injury patient's risk for developing a pressure ulcer (PU) during rehabilitation. DESIGN: This is a prospective observational cohort study conducted at an urban rehabilitation hospital-based specialized spinal cord injury center. The main outcome measure was the onset of a stage 2 or higher PU. RESULTS: Study patients (N = 159) with new (n = 66) and patients with earlier (n = 99) spinal injuries had identical rates at which they acquired a new PU (stage ≥2) in rehabilitation--13.1%. The patients who came to rehabilitation with a PU or myocutaneous flap exhibited a higher rate of developing yet another PU while in rehabilitation (30.2%) than those who came to rehabilitation without an existing PU or flap (6.9%). Logistic regression analysis identified two variables that best predicted a patient's risk at admission for developing a PU during rehabilitation (c = 0.77)--entering rehabilitation with a PU and admission Functional Independence Measure transfers score of less than 3.5. CONCLUSIONS: The greatest risk of developing a new PU in rehabilitation is being admitted with an existing PU followed by admission Functional Independence Measure transfers score of less than 3.5. Using these two variables, one can develop a patient PU risk algorithm at admission that can alert clinicians for the need to enhance vigilance, skin monitoring, and early patient education.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".