SPPB as a predictor of functional loss of hospitalized older adults
Bibliographic record
Abstract
Abstract Introduction: Immobility is associated with adverse outcomes such as loss of functional capacity and longer hospitalization. Objective: To assess intra-hospital mobility at admission as a predictor of loss of functional capacity during older adults´ hospitalization. Methods: A prospective cohort study was conducted, and personal and hospital related risk factors were assessed at admission and discharge. To determine whether Short Physical Performance Battery (SPPB) on admission could predict loss of functional capacity during hospitalization, a ROC curve was performed and area under the curve (AUC) was calculated. Binary logistic regression models were used to identify predictors of loss of functional capacity. Model 1 contained only SPPB. Model 2 SPPB was matched with age, sex, instrumental activity of daily living (IADL), cognition, depression and surgery. Data were entered into SPSS version 18.0. Results: 1,191 patients were included with a mean age of 70.02 (± 7.34). SPPB cutoff point of 6.5 (sensitivity 62%, specificity 54%) identified 593 (49.8%) patients at risk for functional loss. In logistic regression, SPPB alone showed prediction of functional loss (p < 0.001, OR 1.8, 95% CI = 1.5-2.5) between admission and discharge. Model 1 explained between 22 to 32% of the variation in functional capacity. In Model 2, three variables contributed to the loss. SPPB 6.5 increased 1.8 times (95% CI = 1.3-2.4), being a woman increased 1.4 times (95% CI = 1.0-1.8) and not having surgery increased 2 times (95% CI = 1.4-2.8) the chance of having functional loss during hospitalization. Conclusion: SPPB is a good instrument to predict loss of functional capacity in hospitalized older adults.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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 teacher head, 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".