Editorial comment on discussion: ‘Which specific modes of exercise training are most effective for treating low back pain? Network meta-analysis’
Bibliographic record
Abstract
The BJSM recently published1 a network meta-analysis (NMA) on the effect of different modes of exercise training in patients with chronic non-specific low back pain. The emergence of NMA in sport and exercise medicine2 represents important strides towards a ‘shift in what constitutes the highest level of medical evidence’.3 Given the novelty of NMA, frank scientific debate about strengths and limitations is a good thing. A discussion article by Maher and colleagues posed questions about the published NMA.4 As part of our due diligence at BJSM, we posted an Expression of Concern5 on the basis of the Maher et al ’s comments—we flagged that there were questions and we wanted time to consider those questions. An Expression of Concern merely flags to readers that the journal is taking a query seriously. The authors of the NMA addressed all of Maher’s points6 and BJSM recently broadcast the findings of the NMA with an Infographic.7 We also posted a podcast about the NMA and related research https://soundcloud.com/bmjpodcasts/what-are-the-best-exercises-to-manage-low-back-pain-with-aprof-daniel-belavy-episode-443?in=bmjpodcasts/sets/bjsm-1 …
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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 | 0.023 | 0.140 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.035 | 0.030 |
| Insufficient payload (model declined to judge) | 0.020 | 0.016 |
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".