Effectiveness of extrinsic feedback for management of non-specific low back pain: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Low back pain is the greatest cause of years lived with disability worldwide and is linked with high societal and economic burden. Neuromuscular control impairments are a common clinical presentation in patients with non-specific low back pain. Musculoskeletal physiotherapists commonly use feedback as a part of the management of low back disorders. This systematic review will aim to assess the effectiveness of extrinsic biofeedback for reducing pain, disability and recurrence of pain in patients with non-specific low back pain. METHODS AND ANALYSIS: Systematic searches will be performed in CINAHL, Embase, Medline, PsycInfo, Scopus and Web of Science. We will include randomised controlled trial studies, if the study recruited patients with non-specific low back pain; compared extrinsic feedback versus either placebo or control; another intervention; or in addition to an intervention versus that intervention alone; and have used pain, disability scores or low back pain recurrence as outcome measures. We will exclude studies with designs other than randomised controlled trials. We will assess the risk of bias within included studies using the PEDro scale, and the strength of evidence using the Grades of Recommendation, Assessment, Development and Evaluation approach. ETHICS AND DISSEMINATION: Ethical approval and patient consent are not required since this is a systematic review based on published studies. The results of this study will be published in an international peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42017077888.
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 | 0.058 | 0.055 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.022 | 0.015 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.067 | 0.008 |
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".