A Description of the Methodology Used in an Overview of Reviews to Evaluate Evidence on the Treatment, Harms, Diagnosis/Classification, Prognosis and Outcomes Used in the Management of Neck Pain
Bibliographic record
Abstract
BACKGROUND: Neck Pain (NP) is a common musculoskeletal disorder and the literature provides conflicting evidence about its management. OBJECTIVE: To describe the methodology used to conduct an overview of reviews (OvR) and to characterize the distribution and risk of bias profiles across the evidence for all areas of NP management. METHODS: Standard systematic review (SR) methodology was employed. MEDLINE, CINAHL, EMBASE, ILC, Cochrane CENTRAL, and LILACS were searched from 2000 to March 2012; Narrative and SR and clinical practice guidelines (CPG) evaluating the efficacy of treatment (benefits and harms), diagnosis/classification, prognosis, and outcomes were eligible. For treatment, articles were limited to SRs from 2005 forward. Risk of bias of SR was assessed with the AMSTAR; the AGREE II was used to critically appraise the CPGs. RESULTS: From 2476 articles, 508 were eligible for full text screening. A total of 341 articles were included. Treatment (n=117) had the greatest yield. Other clinical areas had less literature (diagnosis=54, prognosis=16, outcomes=27, harms=16). There were no SR for classification and narrative reviews were problematic for this topic. There was great overlap across different databases within each clinical area except for those for outcome measures. Risk of bias assessment using the AMSTAR of eligible SRs showed a similar trend across different clinical areas. CONCLUSION: A summary of methods used to review the literature in five clinical areas of NP management have been described. The challenges of selecting and synthesizing eligible articles in an OvR required customized solutions across different areas of clinical focus.
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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 | 0.247 | 0.402 |
| Meta-epidemiology (narrow) | 0.008 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.031 |
| Bibliometrics | 0.065 | 0.062 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".