Minimal Clinically Important Change for Pain Intensity, Functional Status, and General Health Status in Patients With Nonspecific Low Back Pain
Bibliographic record
Abstract
In Brief Study Design. Cohort study. Objectives. To estimate the Minimal Clinically Important Change (MCIC) of the pain intensity numerical rating scale (PI-NRS), the Quebec Back Pain Disability Scale (QBPDS), and the Euroqol (EQ) in patients with low back pain. Summary of Background Data. MCIC can provide valuable information for researchers, healthcare providers, and policymakers. Methods. Data from a randomized controlled trial with 442 patients with low back pain were used. The MCIC was estimated over a 12-week period, and three different methods were used: 1) mean change scores, 2) minimal detectable change, and 3) optimal cutoff point in receiver operant curves. The global perceived effect scale (GPE) was used as an external criterion. The effect of initial scores on the MCIC was also assessed. Results. The MCIC of the PI-NRS ranged from 3.5 to 4.7 points in (sub)acute patients and 2.5 to 4.5 points in chronic patients with low back pain. The MCIC of the QBPDS was estimated between 17.5 to 32.9 points and 8.5 to 24.6 points for (sub)acute and chronic patients with low back pain. The MCIC for the EQ ranged from 0.07 to 0.58 in (sub)acute patients and 0.09 to 0.28 in patients with chronic low back pain. Conclusion. Reporting the percentage of patients who have made a MCIC adds to the interpretability of study results. We present a range of MCIC values and advocate the choice of a single MCIC value according to the specific context. The Minimal Clinically Important Change (MCIC) for the pain intensity numerical rating scale (PI-NRS), the Quebec Back Pain Disability Scale (QBPDS), and the Euroqol (EQ) was estimated. The magnitude of the MCIC depends on the methods used and initial scores. The choice for a single MCIC value should be made according to the specific context.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".