The Changing Prevalence of Pressure Injury among Ontarians with SCI/D at Rehabilitation Admission: Opportunities for Improvement
Bibliographic record
Abstract
BACKGROUND: Despite preventability, 20-50% of patients with acute spinal cord injury/disease (SCI/D) develop hospital-acquired pressure injuries (PIs). The Spinal Cord Injury Implementation and Evaluation Quality Care Consortium (SCI IEQCC) aimed to mitigate PI risk through patient-reported daily skin checks alongside usual care. METHODS: This quality improvement initiative utilized an interrupted time series design, encompassing adults ≥ 18 years admitted for inpatient rehabilitation across five Ontario sites from 2020 to 2023. Patient demographics, etiology, and impairment data were obtained from a national registry, while participating sites gathered data on PI onset, location, and severity. Run charts depicted temporal trends, and statistical analyses, including chi-square and logistic regression, compared patients with and without PIs. RESULTS: Data from 1767 discharged SCI/D patients revealed that 26% had ≥1 PI, with 59% being prevalent and 41% incident. Most severe PIs (stages III and IV and unstageable) were acquired prior to admission. Process indicator fidelity was reasonable at 68%. Patients with PIs experienced longer hospital stays, lower Functional Independence Measure (FIM) changes, and FIM efficiency during rehabilitation. CONCLUSIONS: PI prevalence is increasing, particularly sacral injuries at admission, while incident cases have decreased since 2021 due to regular skin checks. This trend calls for proactive health system interventions to reduce costs and improve patient outcomes.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".