A Short Physical Performance Battery (SPPB) como preditora da fragilidade em idosos residentes na comunidade
Bibliographic record
Abstract
Introduction: The SPPB provides information about physical function and is a predictor of adverse events in the elderly. Frailty is a multidimensional syndrome that increases susceptibility to diseases and disability. However it may be possible to prevent or postpone frailty if is identified early. Our objective is to analyze SPPB s ability in screening for frailty a community-dwelling young elderly from cities with distinct socioeconomic conditions. Methods: Data were originated from community dwelling adults (65-74 years old) in Canada (Saint Bruno; n = 60) and Brazil (Santa Cruz; n = 64). SPPB was used to assess physical performance. Frailty was defined as the presence of ≥ 3 of these criteria: weight loss, exhaustion, weakness, mobility limitation and low physical activity. One point was given for each criterion met, totalizing a frailty score ranged from 0 to 5. The Linear Regression and Receiver Operating Characteristics analyses were performed to evaluate the SPPB s screening ability. Results: Mean age was 69.48, 10.0% of the Saint Bruno s sample and 28.1% of Santa Cruz s were frail (p = 0.001), the SPPB score means were 9.6 and 8.5 respectively (p = 0.01). SPPB correlated with the frailty score (R2 = 0.33), with better results for Saint Bruno. A cutoff of 9 in SPPB had good sensitivity and specificity in discriminating frail from non frail in Saint Bruno (AUC = 0.81) but showed fair results in Santa Cruz (AUC = 0.61). Conclusion: The SPPB has moderate ability in predicting frailty among older adult s population, and is an useful test to identify people with good functionality and low frailty when SPPB scores are ≥9
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".