Prognostic factors for recovery and non-recovery in patients with non-specific neck pain: a protocol for a systematic literature review
Bibliographic record
Abstract
INTRODUCTION: Neck pain is a common musculoskeletal disorder worldwide. It can result in significant disability and impaired quality of life. More than 50% of patients with neck pain still report symptoms 1 year later despite receiving different forms of non-pharmacological and pharmacological treatment. Identifying patient characteristics that are modifiable or predict recovery and non-recovery for an individual patient might identify ways of improving outcomes. This systematic review aims to comprehensively summarise the existing evidence regarding baseline patient characteristics associated with recovery and non-recovery, as defined by measures of pain intensity, disability and global perceived improvement. METHODS AND ANALYSIS: Six electronic databases, PubMed, CINAHL, PEDro Database, EMBASE, Cochrane Library and Web of Science, will be searched, with terms related to the review question such as neck pain, prognostic or predictive research, from inception to 28 September of 2018. Studies will be included if they have investigated an association between patient characteristics and outcomes, with at least one follow-up time point. Two independent reviewers will screen the titles and abstracts followed by a full-text review to assess papers regarding their eligibility. Data from included papers will be extracted using standardised forms, including study and participants' characteristics, outcomes, prognostic factors and effect size of the association. The risk of bias of each study will be assessed using the Quality in Prognostic Studies tool. A narrative synthesis will be conducted considering the strength, consistency of results and the methodological quality. ETHICS AND DISSEMINATION: This systematic review does not require ethical approval. The results will be disseminated through publication in a peer-review journal, as a chapter of a doctoral thesis and through presentations at national and international conferences. PROSPERO REGISTRATION NUMBER: CRD42018091183.
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 | 0.088 | 0.099 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.018 | 0.019 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.064 | 0.009 |
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".