Análise da prevalência de osteoartrite de joelho em idosos com excesso de peso
Bibliographic record
Abstract
INTRODUCTION: Many are the factors that contribute to the Osteoarthritis’s progression in a patient. The cartilage degradation is one among these factors that result on the impossibility of full regeneration of the cartilage, what forces the OA patients to induce symptoms as articular rigidity, pain and crepitation, along with obesity and ageing, these are some aspects of overweight development. OBJECTIVE: To evaluate the Osteoarthritis in elderly people’s knees. METHODOLOGY: The study was carried out at UNICESUMAR’s Cross-Curricular Laboratory of Intervention and Health Promotion, including elderly over 60 years old with their own consent. Those with incomplete data during the evaluation, that did not answer the survey and those who had already been submitted to Arthroplastic surgery have not been considered in this study. For our purposes, the WOMAC questionnaire (Western Ontario and Macmaster Universities Osteoarthritis Index) was an important tool for research. RESULTS: From the initially subscribed 86 patients the survey were carried out with 47, being 35 female, and 12 male patients. Each one of them was individually evaluated, following WOMAC, under the three domains of analysis: Pain, Articular rigidity and Functionality. Most of the patients did not present either pain or rigidity, as for functionality, little were the limitations in daily activities. CONCLUSION: Age and overweight are both acceleration factors for the osteoarthritis progression in the knees, and in this study, the WOMAC questionnaire scored low due to patients previous participation at the intervention project.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".