Nerve root sedimentation sign on MRI: A triage screen for leg dominant symptoms?
Bibliographic record
Abstract
PURPOSE: Surgical indications for lumbar spinal stenosis are controversial, but most agree that leg dominant pain is a better predictor of success after decompression surgery. The objective of this study is to analyze the ability of the Nerve Root Sedimentation Sign (SedSign) on MRI to differentiate leg dominant symptoms from non-specific low back pain. METHODS: This was a retrospective review of 367 consecutive patients presenting with back and/or leg pain. Baseline clinical characteristics included Oswestry disability index (ODI), visual analog pain scores, EuroQol Group 5-Dimension Self-Report (EQ5D) and Saskatchewan Spine Pathway Classification (SSPc). Inter- and intra-rater reliability for SedSign was 73% and 91%, respectively (3 examiners). RESULTS: SedSign was positive in 111 (30.2%) and negative in 256 (69.8%) patients. On univariate analysis, a positive SedSign was correlated with age, male sex, several ODI components, EQ5D mobility, cross-sectional area (CSA) of stenosis, antero-posterior diameter of stenosis, and SSPc pattern 4 (intermittent leg dominant pain). On multivariate analysis, SedSign was associated with age, male sex, CSA stenosis and ODI walking distance. Patients with a positive SedSign were more likely to be offered surgery after referral (OR 2.65). The sensitivity and specificity for detecting all types of leg dominant pain were 37.4 and 82.8, respectively (ppv 77.5%, npv 43.8%). CONCLUSIONS: Patients with a positive SedSign were more likely to be offered surgery, in particular non-instrumented decompression. The SedSign has high specificity for leg dominant pain, but the sensitivity is poor. As such, its use in triaging appropriate surgical referrals is limited.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".