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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it