Rehabilitative management of back pain in children: protocol for a mixed studies systematic review
Bibliographic record
Abstract
INTRODUCTION: Little is known about effective, efficient and acceptable management of back pain in children. A comprehensive and updated evidence synthesis can help to inform clinical practice. OBJECTIVE: To inform clinical practice, we aim to conduct a systematic review of the literature and synthesise the evidence regarding effective, cost-effective and safe rehabilitation interventions for children with back pain to improve their functioning and other health outcomes. METHODS AND ANALYSIS: We will search MEDLINE, Embase, PsycINFO, CINAHL, the Index to Chiropractic Literature, the Cochrane Controlled Register of Trials and EconLit for primary studies published from inception in all languages. We will include quantitative studies (randomised controlled trials, cohort and case-control studies), qualitative studies, mixed-methods studies and full economic evaluations. To augment our search of the bibliographic electronic databases, we will search reference lists of included studies and relevant systematic reviews, the WHO International Clinical Trials Registry Platform and consult with content experts. We will assess the risk of bias using appropriate critical appraisal tools. We will extract data about study and participant characteristics, intervention type and comparators, context and setting, outcomes, themes and methodological quality assessment. We will use a sequential approach at the review level to integrate data from the quantitative, qualitative and economic evidence syntheses. ETHICS AND DISSEMINATION: Ethics approval is not required. We will disseminate findings through activities, including (1) presentations in national and international conferences; (2) meetings with national and international decision makers; (3) publications in peer-reviewed journals and (4) posts on organisational websites and social media. PROSPERO REGISTRATION NUMBER: CRD42019135009.
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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 | 0.129 | 0.137 |
| Meta-epidemiology (narrow) | 0.008 | 0.007 |
| Meta-epidemiology (broad) | 0.019 | 0.020 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.084 | 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".