MANEJO DA DOR OSTEOARTICULAR EM PESSOAS IDOSAS À PROMOÇÃO DO ENVELHECIMENTO ATIVO
Bibliographic record
Abstract
A populacao idosa no Brasil tem aumentado expressivamente e paralelamente os problemas do aparelho locomotor, como a Osteoartrose (OA), doenca de maior prevalencia, cursando com dor e restricao as atividades diarias. Objetivo. Avaliar o impacto da dor e funcionalidade nos marcadores fisiologicos de Hipertensao Arterial Sistemica (HAS) e Diabetes Mellitus tipo 2 (DM2) em pessoas idosas com OA sobintervencao cinesioterapeutica em um nucleo interdisciplinar de pesquisa-extensao. Metodologia. Estudo misto e intervencionista, aprovado por Comite de Etica, realizado cinco idosas, idade media de 74,6 anos, avaliadas por Escala Visual Analogica (EVA), Indice Western Ontario McMaster Universities Osteoarthritis Index (WOMAC), entrevista semiestruturada, prontuarios clinico e assistidas por um protocolo cinesioterapico de tres sessoes semanais, sessenta minutos cada por tres meses. Os dados foram analisados de forma avaliativa-interpretativa na perspectiva de Miles e Huberman. Resultados. A sobreposicao dos resultados obtidos do WOMAC e EVA evidenciou a intensidade da dor nas participantes pela OA na pre-intervencao impactando nos marcadores HAS e DM2. No pos-intervencao demonstram melhora significante destes marcadores e da dor, ratificando achados da literatura, de que o agravamento e a coexistencia de comorbidades cronicas em face de dor sao potenciais e impactam diretamente a qualidade de saude das pessoas.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".