Precisão da medida de mobilidade no espaço de vida para discriminar fragilidade e sarcopenia em idosos
Bibliographic record
Abstract
Resumo Objetivo Identificar o perfil de mobilidade nos espaços de vida em idosos que vivem na comunidade e estabelecer a precisão dos pontos de corte desse instrumento para discriminar entre níveis de fragilidade, fragilidade em marcha e de risco de sarcopenia. Método Estudo observacional e metodológico com 391 participantes com 72 anos e mais (80,4±4,6), que responderam ao Life Space Assessment (LSA) e a medidas de rastreio de fragilidade e risco de sarcopenia usando respectivamente o fenótipo de fragilidade e o SARC-F. Os pontos de corte para fragilidade e risco de sarcopenia foram determinados por meio da Curva ROC (Receiver Operating Characteristic) com intervalos de confiança de 95%. Resultados A média da pontuação no LSA foi 53,6±21,8. Os pontos de corte de melhor acurácia diagnóstica foram ≤54 pontos para fragilidade em marcha (AUC= 0,645 95%; p<0,001) e ≤60 pontos para risco de sarcopenia (AUC= 0,651 95%; p<0,001). Conclusão A capacidade de idosos de se deslocar nos vários níveis de espaços de vida, avaliado pelo LSA demonstrou ser uma ferramenta viável que pode contribuir no rastreio de fragilidade em marcha e de risco de sarcopenia e, com isso, prevenir desfechos negativos.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".