Sensory descriptors which identify neuropathic pain mechanisms in low back pain: a systematic review
Bibliographic record
Abstract
OBJECTIVE: Descriptors provided by patients with neuropathic low back pain (NLBP) with or without spinally referred leg pain are frequently used by clinicians to help to identify the predominant pain mechanisms. Indeed, many neuropathic screening tools are primarily based on subjective descriptors to determine the presence of neuropathic pain. There is a need to systematically review and analyse the existing evidence to determine the validity of such descriptors in this cohort. METHODS: Ten databases were systematically searched. The review adhered to PRISMA and CRD guidelines and included a risk of bias assessment using QUADAS-2. Studies were included if they contained symptom descriptors from a group of NLBP patients +/- leg pain. Studies had to include a reference test to identity neuropathic pain from other pain mechanisms. RESULTS: Eight studies of 3099 NLBP patients were included. Allodynia and numbness were found to discriminate between NLBP and nociceptive LBP in four studies. Autonomic dysfunction, (changes in the colour or appearance of the skin), was also found to discriminate between the groups in two studies. Dysesthesia identified NLBP in 5/7 respectively. Results from studies were equivocal regarding pain described as hot/burning cold and paroxysmal pain in people with NLBP. CONCLUSION: Subjectively reported allodynia and numbness would suggest a neuropathic pain mechanism in LBP. Dysesthesia would raise the suspicion of NLBP. More research is needed to determine if descriptors suggesting autonomic dysfunction can identify NLBP. There is poor consensus on whether other descriptors can identify NLBP.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".