Evaluation of the <scp>Braden</scp> scale in predicting surgical outcomes in older patients undergoing major head and neck surgery
Bibliographic record
Abstract
BACKGROUND: Being able to predict negative postoperative outcomes is important for helping select patients for treatment as well for informed decision-making by patients. Frailty measures are often time and resource intensive to use as screening measures, whereas the Braden scale, a commonly used measure to assess patients at risk of developing pressure ulcers after surgery, may be a potential tool to predict postoperative complication rates and longer length of stay (LOS) in patients undergoing major head and neck cancer surgery. METHODS: A retrospective analysis of Braden scale scores was performed on a prospectively collected cohort of patients undergoing major head and neck surgery recruited between December 2011 and April 2014. The association of Braden scale score with the primary outcomes of complications and LOS was analyzed using logistic regression and linear regression models on univariate analysis (UVA), respectively. Multivariate analysis (MVA) was performed based on a backward stepwise selection algorithm. RESULTS: < .001) was an independent predictor of increased LOS on UVA, but not on MVA when adjusted for other variables. For overall complications, as well as type of complication, the Braden scale score was not a significant predictor of complications on either UVA or MVA. CONCLUSION: In the sample population, the Braden scale did not demonstrate an ability to predict negative outcomes in head and neck surgery patients. LEVEL OF EVIDENCE: Level 2b individual cohort study.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".