In clinical hand osteoarthritis research, self-reported pain questionnaires do not reflect the patient experience
Bibliographic record
Abstract
Objective Pain in hand osteoarthritis (OA) is evaluated with repeated pain questionnaires. It is unclear whether these questionnaires adequately capture changes in pain recalled by patients. This study investigated whether changes on pain questionnaires (real-time evaluation) correspond to recalled pain. Methods Data from hand OA patients from the HOSTAS cohort (four one-yearly) and HOPE trial (one six-week interval) were used. Pain was measured with the Australian/Canadian hand Osteoarthritis Index (AUSCAN, range 0–20) and a recall question (how is the pain compared with your last visit). Changes in AUSCAN pain were categorized into improved (≤−1), stable or worsened pain (≥1) and compared with the recall question using Cohen’s kappa and percentage agreement. We determined concordance between measurement methods, and investigated associations of mental well-being and illness perceptions with concordance using generalized estimating equations (GEE). Results Of 708 intervals from HOSTAS (307 patients, 82% women, mean age 61.0 years, mean AUSCAN 9.1), AUSCAN changes and recall were concordant in 42% (Cohen’s kappa 0.13). There was concordance in 47% of 86 intervals (Cohen’s kappa 0.14) from the HOPE trial (86 patients, 80% women, mean age 63.5, mean AUSCAN 10.7). The most frequent recall answer was worsened pain in the HOSTAS (60%), improved pain in the HOPE trial (76%). In both studies, AUSCAN pain most frequently improved. Depression and anxiety showed no association with concordance. Conclusion Changes in repeatedly measured AUSCAN pain often differ from the recalled course of pain over the same period. This has profound implications for evaluating patient-reported pain in clinical trials.
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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 | 0.083 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".