Obesity and body mass index: Past and future considerations in osteoarthritis research
Bibliographic record
Abstract
Obesity is an important topic for the osteoarthritis (OA) scientific community. However, the predominant use of body mass index (BMI) to define obesity in OA research is associated with uncertainties and limitations. These include an inability to discern fat and muscle mass, account for sex-differences in fat distribution, or identify adiposity-related health impairments. A focus on BMI in OA research may influence weight bias in clinical practice and impact disparities in access to effective OA treatments. To ensure that our understanding and approaches to improve health outcomes for individuals with or at risk for OA continues to advance in the next decade, future research will need to consider alternative measures beyond BMI for obesity identification and align with evolving obesity science. OA researchers must be aware of issues associated with weight stigma and work to minimize negative generalizations based on BMI.
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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 | 0.181 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".