Faculty Opinions recommendation of The association of osteoarthritis risk factors with localized, regional and diffuse knee pain.
Bibliographic record
Abstract
Objective-To identify determinants of different patterns of knee pain with a focus on risk factors for knee osteoarthritis Design-The Knee Pain Map is an interviewer-administered assessment that asks subjects to characterize their knee pain as localized, regional, or diffuse.A total of 2277 participants from the Osteoarthritis Initiative were studied.We used multinomial logistic regression to examine the relationship between risk factors for OA and knee pain patterns.We examined the bivariate and multivariate relationships of knee pain pattern with age, BMI, sex, race, family history of total joint replacement, knee injury, knee surgery, and hand OA.Results-We compared 2462 knees with pain to 1805 knees without pain.In the bivariate analysis, age, sex, BMI, injury, surgery, and hand OA were associated with at least one pain pattern.In the multivariate model, all of these variables remained significantly associated with at least one pattern.When compared to knees without pain, higher BMI, injury, and surgery were associated with all patterns.BMI had its strongest association with diffuse pain.Older age was less likely to be associated with localized pain while female sex was associated with regional pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.077 | 0.061 |
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".