Stepping Up Counseling and Referral to Effective Physical Activity Interventions for Adults With Osteoarthritis
Bibliographic record
Abstract
To the Editor: In “Stepping Forward: A Scoping Review of Physical Activity in Osteoarthritis,” White and colleagues1 aimed to help patients, policymakers, investigators, and healthcare providers (HCPs) better understand how physical activity (PA) is defined and measured, the benefits of PA for knee osteoarthritis (KOA), the potential role of PA in the development and/or progression of KOA, and the PA guidelines for Americans.2 This correspondence aims to reinforce the importance of PA for adults with arthritis; clarify definitions for “inactive” and “insufficiently active” from the PA guidelines for Americans2; and supplement White and colleagues’ scoping review by (1) highlighting arthritis-appropriate evidence-based interventions (AAEBIs) for PA and self-management education; and (2) providing resources to facilitate HCP screening, counseling, and referral to evidence-based PA interventions for adults with arthritis. Because PA is strongly recommended for management of hand, hip, and KOA,3 it is a public health priority to increase the proportion of adults with arthritis who receive HCP counseling for PA.4,5 For overall health, the PA Guidelines for Americans (2nd edition) recommends all older adults (≥ 65 years), including those with … Address correspondence to Dr. E.A. Fallon, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health, Healthy Aging Branch, 4770 Buford Hwy NE, Mailstop S-107-6, Atlanta, GA 30341, USA. Email: UHC6{at}cdc.gov.
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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 | 0.012 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".