An Overview of Systematic Reviews on Prognostic Factors in Neck Pain: Results from the International Collaboration on Neck Pain (ICON) Project
Bibliographic record
Abstract
Given the challenges of chronic musculoskeletal pain and disability, establishing a clear prognosis in the acute stage has become increasingly recognized as a valuable approach to mitigate chronic problems. Neck pain represents a condition that is common, potentially disabling, and has a high rate of transition to chronic or persistent problems. As a field of research, prognosis in neck pain has stimulated several empirical primary research papers, and a number of systematic reviews. As part of the International Consensus on Neck (ICON) project, we sought to establish the general state of knowledge in the area through a structured, systematic review of systematic reviews (overview). An exhaustive search strategy was created and employed to identify the 13 systematic reviews (SRs) that served as the primary data sources for this overview. A decision algorithm for data synthesis, which incorporated currency of the SR, risk of bias assessment of the SRs using AMSTAR scoring and consistency of findings across SRs, determined the level of confidence in the risk profile of 133 different variables. The results provide high confidence that baseline neck pain intensity and baseline disability have a strong association with outcome, while angular deformities of the neck and parameters of the initiating trauma have no effect on outcome. A vast number of predictors provide low or very low confidence or inconclusive results, suggesting there is still much work to be done in this field. Despite the presence of multiple SR and this overview, there is insufficient evidence to make firm conclusions on many potential prognostic variables. This study demonstrates the challenges in conducting overviews on prognosis where clear synthesis critieria and a lack of specifics of primary data in SR are barriers.
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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 | 0.075 | 0.223 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.017 |
| Bibliometrics | 0.043 | 0.042 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".