Examination of a Subgroup of Patients With Chronic Low Back Pain Likely to Benefit More From Pilates-Based Exercises Compared to an Educational Booklet
Bibliographic record
Abstract
Objective To investigate whether 2 previously published classification approaches, the updated treatment-based classification system and a Pilates subgroup defined by a preliminary clinical prediction rule, could identify patients with chronic low back pain who would benefit more from Pilates exercises compared to an educational booklet. Design Secondary analysis of a randomized controlled trial. Methods Two hundred twenty-two patients received advice and were randomly allocated to a group that received an educational booklet with no additional treatment (n = 74) or a group that received Pilates-based exercise treatment (n = 148) 2 or 3 times a week. At baseline, using a treatment-based classification system, patients were classified as having a good prognosis (positive movement control) or a poor prognosis. Similarly, using the Pilates clinical prediction rule, patients were classified as having a good prognosis (positive) or a poor prognosis (negative). The analysis was conducted using linear regression models to analyze the interaction between subgroup characteristics and treatment effect size, with changes in pain and disability from baseline to 6 weeks after randomization as dependent variables. Results None of the interaction terms for pain and disability were statistically significant. The treatment effect of Pilates versus an educational booklet was similar in all subgroups. Conclusion The treatment-based classification system and the Pilates clinical prediction rule did not differentiate subgroups of patients with chronic low back pain who were more or less likely to benefit more from Pilates compared to an educational booklet. J Orthop Sports Phys Ther 2020;50(4):189–197. Epub 23 Aug 2019. doi:10.2519/jospt.2019.8839
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".