Prognostic factors of pain, disability, and poor outcomes in persons with neck pain – an umbrella review
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to identify prognostic factors pertaining to neck pain from systematic reviews. DATA SOURCES: A search on PubMed, Scopus, and CINAHL was performed on June 27, 2024. Additional grey literature searches were performed. REVIEW METHODS: We conducted an umbrella review and included systematic reviews reporting the prognostic factors associated with non-specific or trauma-related neck pain and cervical radiculopathy. Prognostic factors were sorted according to the outcome predicted, the direction of the predicted outcome (worse, better, inconsistent), and the grade of evidence (Oxford Center of Evidence). The predicted outcomes were regrouped into five categories: pain, disability, work-related outcomes, quality of life, and poor outcomes (as "recovery"). Risk of bias analysis was performed with the ROBIS tool. RESULTS: We retrieved 884 citations from three databases, read 39 full texts, and included 16 studies that met all selection criteria. From these studies, we extracted 44 prognostic factors restricted to non-specific neck pain, 47 for trauma-related neck pain, and one for cervical radiculopathy. We observed that among the prognostic factors, most were associated with characteristics of the condition, cognitive-emotional factors, or socio-environmental and lifestyle factors. CONCLUSION: This study identified over 40 prognostic factors associated mainly with non-specific neck pain or trauma-related neck pain. We found that a majority were associated with worse outcomes and pertained to domains mainly involving cognitive-emotional factors, socio-environmental and lifestyle factors, and the characteristics of the condition to predict outcomes and potentially guide clinicians to tailor their interventions for people living with neck pain.
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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 | 0.019 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.026 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".