A Roland Morris Disability Questionnaire Target Value to Distinguish between Functional and Dysfunctional States in People with Low Back Pain
Bibliographic record
Abstract
PURPOSE: To estimate a threshold Roland Morris Disability Questionnaire (RMQ) value that could be used to classify patients with low back pain (LBP) as functional or dysfunctional. METHODS: In this secondary analysis of data from a study that estimated clinically important RMQ change scores, participants were adults with LBP attending one of three physical therapy clinics. Diagnostic test methodology and a reference standard of goals met were applied to estimate a threshold RMQ value that best distinguished between participants with a functional status and those whose status was dysfunctional. RESULTS: Of 143 participants, 104 (73%) met their goals. An RMQ threshold value of 4/24 best distinguished between those who met their goals and those who did not. Sensitivity and specificity for a threshold score of 4 were 94% (95% CI, 88-98) and 69% (95% CI, 52-83), respectively. CONCLUSIONS: A threshold value of 4 RMQ points provided a reasonably accurate classification of patients. Further research is necessary to cross-validate this estimate and to examine the stability of the estimated value in people with diverse functional demands.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".