Diagnostic Accuracy of the Slump Test for Identifying Neuropathic Pain in the Lower Limb
Bibliographic record
Abstract
STUDY DESIGN: Diagnostic accuracy study with nonconsecutive enrollment. OBJECTIVES: To assess the diagnostic accuracy of the slump test for neuropathic pain (NeP) in those with low to moderate levels of chronic low back pain (LBP), and to determine whether accuracy of the slump test improves by adding anatomical or qualitative pain descriptors. BACKGROUND: Neuropathic pain has been linked with poor outcomes, likely due to inadequate diagnosis, which precludes treatment specific for NeP. Current diagnostic approaches are time consuming or lack accuracy. METHODS: A convenience sample of 21 individuals with LBP, with or without radiating leg pain, was recruited. A standardized neurosensory examination was used to determine the reference diagnosis for NeP. Afterward, the slump test was administered to all participants. Reports of pain location and quality produced during the slump test were recorded. RESULTS: The neurosensory examination designated 11 of the 21 participants with LBP/sciatica as having NeP. The slump test displayed high sensitivity (0.91), moderate specificity (0.70), a positive likelihood ratio of 3.03, and a negative likelihood ratio of 0.13. Adding the criterion of pain below the knee significantly increased specificity to 1.00 (positive likelihood ratio = 11.9). Pain-quality descriptors did not improve diagnostic accuracy. CONCLUSION: The slump test was highly sensitive in identifying NeP within the study sample. Adding a pain-location criterion improved specificity. Combining the diagnostic outcomes was very effective in identifying all those without NeP and half of those with NeP. Limitations arising from the small and narrow spectrum of participants with LBP/sciatica sampled within the study prevent application of the findings to a wider population. LEVEL OF EVIDENCE: Diagnosis, level 4-.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".