Dietary Fiber Intake in Relation to Knee Pain Trajectory
Bibliographic record
Abstract
OBJECTIVE: Dietary fiber may reduce knee pain, in part by lowering body weight and reducing inflammation. In this study, we assessed whether fiber intake was associated with patterns of knee pain development. METHODS: In a prospective, multicenter cohort of 4,796 men and women ages 45-79 years with or at risk of knee osteoarthritis, participants underwent annual followups for 8 years. Dietary fiber intake was estimated using a validated food frequency questionnaire at baseline. Group-based trajectory modeling was used to identify Western Ontario and McMaster Universities Osteoarthritis Index pain trajectories, which were assessed for associations with dietary fiber intake using polytomous regression models. RESULTS: Of the eligible participants (4,470 persons and 8,940 knees, mean ± SD age 61.3 ± 9.1 years, 58% women), 4.9% underwent knee replacement and were censored at the time of surgery. Four distinct knee pain patterns were identified: no pain (34.5%), mild pain (38.1%), moderate pain (21.2%), and severe pain (6.2%). Dietary total fiber was inversely related to membership in the moderate or severe pain groups (P ≤ 0.006 for trend for both). Subjects in the highest versus those in the lowest quartile of total fiber intake had a lower risk of belonging to the moderate pain pattern group (odds ratio [OR] 0.76 [95% confidence interval (95% CI) 0.61-0.93]) and severe pain pattern group (OR 0.56 [95% CI 0.41-0.78]). Similar results were found with grain fiber and these 2 pain pattern groups. CONCLUSION: Our findings suggest that a high intake of dietary total or grain fiber, particularly the recommended daily fiber average intake of 25 gm per day, is associated with a lower risk of developing moderate or severe knee pain over time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".