Knee pain and future decline in higher-level functional competence in community-dwelling older Japanese: the Kurabuchi cohort study
Bibliographic record
Abstract
BACKGROUND: The effect of knee osteoarthritis, which causes knee pain, on higher-level functional competence (HLFC) is not clear. OBJECTIVE: To clarify the effect of knee pain on HLFC in older people. DESIGN: Community-based prospective cohort study. SETTING: Kurabuchi town, Gumma prefecture, Japan. SUBJECTS: Community-dwelling individuals aged 65 and older. METHODS: A total of 808 residents participated to the baseline examinations. The frequency of knee pain, degree of pain and functional impairment resulting from the pain were asked at baseline (2005-2006) via a self-administered questionnaire in Japanese based on an English version of the Western Ontario and McMaster Universities Osteoarthritis Index. Information on HLFC at baseline and during home visits were collected annually until 2014 with the Tokyo Metropolitan Institute of Gerontology Index of Competence. The association between baseline knee pain and HLFC decline was assessed with a Cox proportional hazards model. RESULTS: Two factors, persistent knee pain and severe functional impairment caused by the pain, were significantly associated with future declines in total HLFC, with adjusted hazard ratios (95% confidence intervals) of 1.51 (1.08-2.11) and 1.49 (1.10-2.00). In analysis by subcategory, persistent knee pain had a significant adverse effect on participants' intellectual and social activities, and that severe physical functional impairment also had a significant impact on social activities. CONCLUSIONS: The clear association of the frequency of knee pain and resultant functional impairment with future HLFC decline indicates that collecting information about these factors may be useful in identifying older people at high risk of future HLFC decline.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".