Bibliographic record
Abstract
A osteoartrite (OA) é uma das doenças reumáticas mais comuns, sendo importante causa de dor crônica, incapacidade e redução da qualidade de vida. A padronização de métricas de avaliação da morbidade ocasionada por esta doença é indispensável para a realização de estudos clínicos, ainda que seu uso no dia a dia seja menos frequente na prática do reumatologista. Para este fim, as métricas precisam demonstrar validade e reprodutibilidade, além de avaliar os domínios principais de acometimento da doença. Nesse contexto, vários índices de avaliação surgiram nas últimas décadas. Nesta revisão, apresentamos os domínios avaliados mais relevantes em estudos clínicos de OA, definidos pelo Outcome Measures in Rheumatology (OMERACT) e Osteoarthritis Research Society International (OARSI), bem como as métricas mais comumente utilizadas, destacando as particularidades de cada uma. Além da Escala Visual Analógica (EVA) para dor, destacamos o Western Ontario and McMaster Universities Arthritis Index (WOMAC), Knee Injury and Osteoarthritis Outcome Score (KOOS), Hip Disability and Osteoarthritis Outcome Score (HOOS), Escore de Lequesne, Australian/Canadian Hand Osteoarthritis Index (AUSCAN), Michigan Hand Outcomes Questionnaire (MHQ) e o Functional Index for Hand Osteoarthritis (FIHOA). Entre as que avaliam imagem, destacamos a classificação de Kellgren-Lawrence (KL), o MRI Osteoarthritis Knee Score (MOAKS) e o Whole-Organ Magnetic Resonance Imaging Score (WORMS). Unitermos: Osteoartrite. Métricas. Desfechos. Dor. Funcionalidade.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".