Do Numerical Rating Scales and the Roland-Morris Disability Questionnaire capture changes that are meaningful to patients with persistent back pain?
Bibliographic record
Abstract
OBJECTIVES: To investigate patients' views about two common outcome measures used for back pain: Numerical Rating Scales for pain and the Roland-Morris Disability Questionnaire. SUBJECTS: Thirty-six working adults who had previously sought primary care for back pain and who could speak and read English. METHOD: Eight focus groups were conducted to explore participants' views about the 11-point Numerical Rating Scales and the 24-item Roland-Morris Disability Questionnaire. Each group was led by a facilitator and an interview topic guide was used. Audio recordings of focus groups were transcribed verbatim. Framework analysis was used to chart participants' views and an interpretive analysis performed to explain the findings. RESULTS: Participants reported that neither the Roland-Morris nor the Numerical Rating Scales captured the complex personal experience of pain or relevant changes in their condition. The time-frame of assessment was identified as particularly problematic and the Roland-Morris did not capture relevant functional domains. CONCLUSION: This study provides empirical data that working adults with persistent back pain consider these clinical outcome measures largely inadequate. These measures currently used for back pain may contribute to misleading conclusions about treatment efficacy and patient recovery.
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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 | 0.019 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".