Effectiveness of interventions for middle-aged and ageing population with neck pain: a systematic review and network meta-analysis protocol
Bibliographic record
Abstract
INTRODUCTION: Neck pain (NP) is a common musculoskeletal complaint and is increasing in prevalence. Current clinical practice guidelines and systematic reviews recommended conservative, pharmacological and invasive interventions for individuals with NP. However, optimal management specifically for those who are middle-aged or older adults (≥45 years) is not available; and important considering our ageing population. METHODS AND ANALYSIS: A systematic review with network meta-analysis (NMA) will be conducted following the Cochrane guidelines. Eligibility criteria include randomised controlled/clinical trials evaluating any of acute (<3 months) or chronic (≥3 months) non-specific NP, whiplash associated disorders, cervical radiculopathy and cervicogenic headache. Any interventions and outcome measures detailed within The International Classification of Functioning, Disability and Health domains will be included. Two independent reviewers will search key databases (AMED, CENTRAL, CINAHL, Embase, MEDLINE, PEDro and PsycINFO), grey literature, key journals and reference lists in May 2022. Two reviewers will decide eligibility and assess risk of bias (ROB) of included studies. The kappa statistic will be used to evaluate agreement between the reviewers at each stage. Data will be extracted by one reviewer and checked for accuracy by a second reviewer. Descriptive data and ROB will be summarised and tabulated. Traditional pairwise meta-analysis using random-effect model will be performed for all direct comparisons, and NMA using a frequentist random-effect model then performed based on NP classification where possible. A network of traditional pairwise meta-analysis allows comparisons of multiple interventions from both direct and indirect evidence to provide a hierarchal establishment for enhancing decision making of clinical practitioners. ETHICS AND DISSEMINATION: Ethic approval is not required as the study is a literature review. The findings will be shared with the national and international researchers, healthcare professionals and the general public through publishing in a peer-reviewed journal and presentations at conferences. PROSPERO REGISTRATION NUMBER: CRD42021284618.
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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 | 0.066 | 0.087 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.022 | 0.026 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.081 | 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".