What WOMAC pain score should make a patient eligible for a trial in knee osteoarthritis?
Bibliographic record
Abstract
OBJECTIVE: To evaluate different Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain thresholds as eligibility criteria for a knee osteoarthritis (OA) trial and their effect on number of subjects recruited. METHODS: We screened subjects with knee pain using the Likert version of the WOMAC and scored all subjects based on the severity of pain with each of the 5 WOMAC activities. For each of 4 alternative definitions of eligibility, we tested how many subjects would be eligible for a trial. RESULTS: Two hundred thirty-four subjects with chronic knee pain completed the WOMAC pain survey. If we required a score of > or = 4 and at least 2 activities with at least moderate pain, we found 128 of these subjects were eligible. If we required only one activity with moderate pain, the number increased to 139 (by 9%), and further to 161 (by 26%) if we required the same overall WOMAC score but no activity with at least moderate pain. The most common activity causing moderate or greater pain was going up or down stairs. CONCLUSION: The number of subjects recruitable for an OA trial depends on the WOMAC pain threshold required. Raising the threshold will lower the number of subjects modestly, but include more persons with moderate to severe pain. Lowering it may include many with only mild pain with activity.
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 | 0.028 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".