Letter in response to: ‘Which specific modes of exercise training are most effective for treating low back pain? Network meta-analysis’ by Owen<i>et al</i>
Bibliographic record
Abstract
The recent network meta-analysis by Owen and colleagues1 included 89 trials of exercise for chronic low back pain (LBP) and reported low quality evidence that Pilates, stabilisation, resistance and aerobic exercises are the most effective treatments for these patients. We were surprised by how few trials were included and even more surprised by how large the estimates of treatment effect were. For example, with Pilates the effect is reported to be 1.86 standardised mean difference (SMD) in the abstract and 2.32 SMD in online supplementary table 5. These estimates are about 3–4 times effect sizes normally reported for exercise interventions in LBP and so we took a closer look at the review to try to understand what had happened. That investigation revealed some important issues that we would like to share with readers. First, the review has missed a lot of relevant trials. The Cochrane review of exercise for chronic LBP that is currently underway has identified over 350 trials, whereas the Owen review included only 89. Even applying the restrictive selection criteria of the Owen …
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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 | 0.007 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.031 | 0.026 |
| Insufficient payload (model declined to judge) | 0.010 | 0.012 |
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".