Nerve Root Sedimentation Sign for the Diagnosis of Lumbar Spinal Stenosis
Bibliographic record
Abstract
In Brief Study Design. Retrospective review of magnetic resonance images. Objective. Examine the diagnostic accuracy, discriminative ability, and reliability of the sedimentation sign in a sample of patients with clinically diagnosed lumbar spinal stenosis (LSS), low back pain (LBP), and vascular claudication, and in asymptomatic controls. Summary of Background Data. The nerve root sedimentation sign (SedSign) was recently described as a new diagnostic test for LSS; however, the degree to which this sign is sensitive and specific in diagnosis of LSS is unknown. Methods. All LSS images were obtained from subjects who had clinically diagnosed LSS confirmed on imaging by a spine specialist. The other images were obtained from people with LBP but no LSS, people with severe vascular claudication, and asymptomatic participants. Three blinded raters independently assessed the images. A positive sign was defined as the absence of nerve root sedimentation at the level above or below the level of maximum stenosis. Results. Images from 148 subjects were reviewed (67 LSS, 31 LBP, 4 vascular, and 46 asymptomatic). Intrarater reliability for the sign ranged from κ= 0.87 to 0.97 and inter-rater reliability from 0.62 to 0.69. Sensitivity ranged from 42% to 66%, and specificity ranged from 49% to 78%. Sensitivity improved to a range of 60% to 96% when images with only a smallest cross-sectional area of the dural sac less than 80 mm2 were included. The sign was able to differentiate (P = 0.004) between LSS and asymptomatic controls but not between LSS and LBP or between LSS and vascular claudication. Conclusion. The SedSign was shown to have high intrarater reliability and acceptable inter-rater reliability. The Sign appears most sensitive in defining severe LSS cases, yet may not aid in the differential diagnosis of LSS from LBP or vascular claudication, or add any specific diagnostic information beyond the traditional history, physical examination, and imaging studies that are standard in LSS diagnosis. Level of Evidence: 4 The sedimentation sign was shown to have high intrarater reliability and appears most sensitive in defining severe lumbar spinal stenosis (LSS) cases. Yet, this sign may not aid in the differential diagnosis of LSS from low back pain or vascular claudication or add any specific diagnostic information beyond the history, physical examination, and imaging studies that are standard in LSS diagnosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".