Comparing the Braden and Jackson/Cubbin Pressure Injury Risk Scales in Trauma-Surgery ICU Patients
Bibliographic record
Abstract
BACKGROUND: The occurrence of pressure injury in the critical care environment has multiple risk factors. Prevention requires reliable assessment tools to help predict injury risk. The Braden scale, a commonly used risk assessment tool, has been shown to have poor predictive properties in critical care patients. The Jackson/Cubbin scale was developed specifically for pressure injury risk stratification in critically ill patients and has demonstrated acceptable predictive properties in the general critical care population but has not been examined in critically ill trauma-surgical patients. OBJECTIVE: To compare the predictive properties of the Braden and Jackson/Cubbin scales in a trauma-surgical critical care population. METHODS: A retrospective medical records review was performed to evaluate the clinical characteristics of 366 trauma-surgical critical care patients. Additionally, the negative predictive value, positive predictive value, sensitivity, specificity, and receiver operating characteristic curve with area under the curve of the Braden and Jackson/Cubbin scales were determined. RESULTS: The sample consisted of primarily middle-aged (mean [SD], 56 [19] years) men (64%) admitted after trauma (71%). The participants who developed pressure injuries were older, more often required vasopressors and mechanical ventilation, and were less mobile. Predictive properties for the Braden and Jackson/Cubbin scales, respectively, were as follows: negative predictive value, 78% versus 87%; positive predictive value, 53% versus 66%; sensitivity, 17% versus 54%; specificity, 95% versus 92%; and area under the curve, 0.710 versus 0.793. CONCLUSION: The Jackson/Cubbin scale demonstrated superior predictive properties and discrimination compared with the Braden scale for pressure injury risk prediction in critically ill trauma-surgical patients.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".