P.104 The diagnostic value of the nerve root sedimentation sign for symptomatic lumbar stenosis
Bibliographic record
Abstract
Background: Previous studies have shown varied results with respect to the diagnostic utility of a positive nerve root sedimentation sign (SedSign) on MRI for symptomatic lumbar stenosis. The objective of this study was to analyze the clinical characteristics of SedSign utilizing a validated classification for low back and leg pain (Saskatchewan Spine Pathway classification; SSPc). Methods: This was a retrospective review of prospectively-collected data in 367 consecutive adult patients presenting to a spine surgeon with back and/or leg pain between January 1, 2012 and May 31, 2018. 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%) of patients. On the univariate analysis, a positive SedSign was correlated with age, male sex, several components of ODI, EQ5D mobility, cross-sectional area (CSA) of stenosis, and SSPc pattern 4 (intermitted leg dominant pain). On multivariate analysis, SedSign was associated with age, male sex, CSA stenosis and ODI sub-score for walking distance. The sensitivity and specificity of SedSign for neurogenic claudication was 50.3 and 82.9, respectively (positive predictive value 65.8%, negative predictive value 71.9%). Conclusions: The SedSign has high specificity for neurogenic claudication, but the sensitivity is poor.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".