P.174 The nerve root sedimentation sign on MRI is not only correlated with neurogenic claudication: association with leg dominant mechanical pain
Bibliographic record
Abstract
Background: A correlation between the nerve root sedimentation sign (SedSign) and neurogenic claudication has been demonstrated; though it did not account for leg-dominant pain. This study analyzed the utility of SedSign to diagnose leg-dominant pain using validated classification systems. Methods: We retrospectively reviewed prospective data from 367 patients with back or leg pain collected between January 1, 2012 to May 31, 2018. Baseline characteristics included SSPc (Saskatchewan Spine Pathway classification), Oswestry disability index (ODI), visual analogue pain scores (VAS), and EuroQol Group 5-Dimension Self-Report (EQ5D). Inter- and intra-rater reliability for SedSign was 73% and 91%. Results: SedSign was positive in 111 (30.2%) and negative in 256 (69.8%) patients. Univariate analysis showed a correlation between SedSign and age, male sex, ODI, EQ5D, cross-sectional area (CSA) of stenosis, antero-posterior diameter of stenosis, and leg-dominant pain; negative SedSign was correlated with back-dominant pain. Multivariate analysis revealed an association between SedSign and age, male sex, CSA stenosis, and ODI walking distance. The sensitivity, specificity, positive and negative predictive values of SedSign for leg-dominant pain were 33.5%, 83.2%, 77.0%, and 57.3%. Conclusions: SedSign has high specificity but low sensitivity for leg-dominant pain. Despite a similar correlation between SedSign and neurogenic claudication or sciatica, significance was lost on multivariate analysis.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".