Patient questionnaires in osteoarthritis: what patients teach doctors about their osteoarthritis on a multidimensional health assessment questionnaire (MDHAQ) in clinical trials and clinical care.
Bibliographic record
Abstract
A patient history generally provides the most important information in diagnosis and management of patients with most rheumatic diseases, including osteoarthritis (OA). Patient history components can be expressed as quantitative, structured, "scientific" data, rather than "subjective" narrative descriptions, using patient self-report questionnaires. The Western Ontario McMaster (WOMAC) questionnaire is used in all OA clinical trials, and the health assessment questionnaire (HAQ) in all rheumatoid arthritis (RA) clinical trials, as "disease-specific" questionnaires. However, both questionnaires include scores for physical function function and pain; physical function scores are correlated highly significantly at r=0.78 in both RA and OA patients, while WOMAC pain scores are correlated with HAQ visual analogue scale (VAS) pain scores at r=0.73 in OA and r=0.71 in RA. Therefore, the WOMAC and HAQ may be regarded as largely "generic" questionnaires, at least for people with arthritis. Since it is not feasible to ask patients with different diagnoses to complete different care questionnaires in busy clinical settings, a single multidimensional HAQ (MDHAQ), derived from the HAQ and largely similar and informative in all rheumatic diseases, has been used in all rheumatology patients in several settings. The MDHAQ also has been incorporated into two OA clinical trials, with virtually identical results to the WOMAC. In routine clinical care, MDHAQ scores have documented that the disease burden of OA is comparable to RA in terms of scores for pain, physical function, and RAPID3 (routine assessment of patient index data) an index of pain, function and patient global assessment. Further observations indicate capacity of the MDHAQ to recognise fibromyalgia similarly to formal fibromyalgia criteria, as well as the ineffectiveness of opioids in OA, and similar prevalence of depression and other psychological issues in OA to RA. These findings also illustrate the value of a database of MDHAQ data for retrospective analysis of serendipitous observations from routine clinical care.
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 | 0.055 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".