Pain descriptors and determinants of pain sensitivity in knee osteoarthritis: a community-based cross-sectional study
Bibliographic record
Abstract
Abstract Objectives The aim was to explore pain characteristics in individuals with knee OA (KOA), to compare pain sensitivity across individuals with KOA, individuals with chronic back pain (CBP) and pain-free individuals (NP) and to examine the relationship between clinical characteristics and pain sensitivity and between pain characteristics and pain sensitivity in KOA. Methods We carried out a cross-sectional, community-based online survey. Two data sets were combined, consisting of Dutch individuals ≥40 years of age, who were experiencing chronic knee pain (KOA, n = 445), chronic back pain (CBP, n = 504) or no pain (NP, n = 256). Demographic and clinical characteristics, global health, physical activity/exercise and pain characteristics, including intensity, spreading, duration, quality (short-form McGill pain questionnaire) and sensitivity (pain sensitivity questionnaire), were assessed. Differences between (sub)groups were examined using analyses of variance or χ2 tests. Regression analyses were performed to examine determinants of pain sensitivity in the KOA group. Results The quality of pain was most commonly described as aching, tender and tiring–exhausting. Overall, the KOA group had higher levels of pain sensitivity compared with the NP group, but lower levels than the CBP group. Univariately, pain intensity, its variability and spreading, global health, exercise and having co-morbidities were weakly related to pain sensitivity (standardized β: 0.12–0.27). Symptom duration was not related to pain sensitivity. Older age, higher levels of continuous pain, lower levels of global health, and exercise contributed uniquely, albeit modestly, to pain sensitivity (P < 0.05). Conclusion Continuous pain, such as aching and tenderness, in combination with decreased physical activity might be indicative for a subgroup of individuals at risk for pain sensitivity and, ultimately, poor treatment outcomes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".