Age does not modify the effects of treatment on pain in patients with low back pain: Secondary analyses of randomized clinical trials
Bibliographic record
Abstract
Abstract Background While many treatment options have been advocated to speed recovery in adults with back pain, it is still unclear whether older patients with back pain respond differently to treatment when compared to younger patients. Aims This study aims to evaluate if age modifies response to treatment in patients with back pain, by conducting secondary analyses of seven randomized clinical trials of common interventions for back pain. Methods Data from 1233 participants were sourced from two randomized clinical trials on patients with acute back pain and five on patients with persistent back pain. Trials were conducted between 2001 and 2010 and assessed conservative treatments for back pain, including exercise, spinal manipulative therapy and anti‐inflammatory drugs. Individual participant data analyses were performed using a ‘one‐stage’ approach including all available data in a single two‐level model, with participants being one level and trials being the second level. Only pain outcomes assessed immediately after treatment were included in the analyses. Results Mean combined age of included participants was 49 years (SD: 15.4). Multivariate fractional polynomial analyses revealed no significant interaction (p > 0.05) between age and treatment effect sizes in patients with low back pain for any of the treatment comparisons. Conclusion These results offer preliminary evidence suggesting that the generally small effects of conservative treatments for low back pain are in fact observed across all age groups.
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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 | 0.135 | 0.175 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.031 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".