Red flags for potential serious pathologies in people with neck pain: a systematic review of clinical practice guidelines
Bibliographic record
Abstract
Introduction: We conducted a systematic review of clinical practice guidelines to identify red flags for serious pathologies in neck pain mentioned in clinical practice guidelines, to evaluate agreement in red flag recommendations across guidelines, and to investigate the level of evidence including what study type the recommendations are based on. Methods: We searched for guidelines focusing on specific and nonspecific neck pain in MEDLINE, EMBASE, and PEDro up to June 9, 2023. Additionally, we searched for guidelines through citation tracking strategies, by consulting experts in the field, and by checking guideline organization databases. Results: We included 29 guidelines, 12 of which provided a total of 114 red flags for fracture (n = 17), cancer (n = 21), spinal infection (n = 14), myelopathy (n = 15), injury to the spinal cord (n = 1), artery dissection (n = 7), intracranial pathology (n = 3), inflammatory arthritis (n = 2), other systemic disease (n = 6), or unrelated to a specific condition (n = 19). Overall, there is very little agreement (median Fleiss' kappa of 0) between guidelines on the red flags to screen for serious pathologies. Conclusion: Red flags were mainly supported by expert opinions. We also observed a general lack of consensus among guidelines regarding which red flags to endorse. Considering the current limitations of the evidence, specific recommendations on which red flags to use cannot be provided, except for using the Canadian C-Spine rule for screening posttraumatic fractures.
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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 | 0.052 | 0.264 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.026 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".