Diagnostic utility of diagnostic investigations to identify neuropathic pain in low back-related leg pain: protocol for a systematic review
Bibliographic record
Abstract
INTRODUCTION: Neuropathic pain in low back-related leg pain has gained increasing interest in contemporary research. Identification of neuropathic pain in low back-related leg pain is essential to inform precision management. Diagnostic investigations are commonly used to identify neuropathic pain in low back-related leg pain; yet the diagnostic utility of these investigations is unknown. This systematic review aims to investigate the diagnostic utility of diagnostic investigations to identify neuropathic pain in low back-related leg pain. METHODS AND ANALYSIS: This protocol has been designed and reported in accordance with the Cochrane Handbook for Diagnostic Test Accuracy studies, Centre for Reviews and Dissemination and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols checklist, respectively. The search strategy will involve two independent reviewers searching electronic databases (CINAHL, EMBASE, MEDLINE, Web of Science, Cochrane Library, AMED, Pedro), key journals (Spine, The Clinical Journal of Pain, PAIN, European Journal of Pain, The Journal of Pain, Musculoskeletal Science and Practice) and grey literature (British National Bibliography for report literature, OpenGrey, EThOS) from inception to 31 July 2023 to identify studies. Studies evaluating the diagnostic accuracy of diagnostic investigation to identify neuropathic pain in patients with low back-related leg pain will be eligible, studies not written in English will be excluded. The reviewers will extract the data from included studies, assess risk of bias (Quality Assessment of Diagnostic Accuracy Studies 2) and determine confidence in findings (Grading of Recommendations, Assessment, Development and Evaluation guidelines). Methodological heterogeneity will be assessed to determine if a meta-analysis is possible. If pooling of data is not possible then a narrative synthesis will be done. ETHICS AND DISSEMINATION: Ethical approval is not required. Findings will be published in a peer-reviewed journal, presented at relevant conferences and shared with the Patient Partner Advisor Group at Western University, Canada. PROSPERO REGISTRATION NUMBER: CRD42023438222.
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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 | 0.088 | 0.160 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.019 | 0.024 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.059 | 0.008 |
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".