Enhancing Annular Fissures and High-Intensity Zones: Pain, Internal Derangement, and Anesthetic Response at Provocation Lumbar Discography
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: A high-intensity zone identified on preprocedural MR imaging is known to correlate with pain at provocation lumbar discography. The correlation between enhancing annular fissures and pain at provocation lumbar discography has not been comprehensively evaluated. The purpose of this study was to assess the pain response and imaging features at enhancing annular fissure nonoperated disc levels identified on preprocedural MR imaging with comparison with the high-intensity zone and nonenhancing disc levels in patients referred for provocation lumbar discography. MATERIALS AND METHODS: One-hundred nonoperated discs in 44 patients were retrospectively evaluated for an enhancing annular fissure on sagittal postcontrast T1-weighted pre-discogram MR imaging. Enhancing annular fissure discs were graded on the sagittal T2-weighted sequence (Grade 4: like CSF to Grade 1: negative/barely visible) for high-intensity-zone conspicuity. High-intensity-zone detection was performed independently. In the primary assessment, enhancing annular fissure and high-intensity zones were associated with pain response at provocation lumbar discography. Additional analysis included intradiscal anesthetic response and postdiscogram CT appearance. RESULTS: Thirty-nine discs demonstrated an enhancing annular fissure, with 23/39 demonstrating a high-intensity zone. The presence of a high-intensity zone predicted severe pain (concordant + nonconcordant; P = .005, sensitivity of 40%, specificity of 94%) and concordant pain (P = .007, sensitivity of 39%, specificity of 86%) at provocation lumbar discography. Enhancing annular fissures without a detected high-intensity zone were more frequently observed among severely painful (50%) and concordant (36%) discs than among discs negative for pain (9%; P = .01). This finding resulted in a substantially greater overall sensitivity of enhancing annular fissures for severe (P < .001, 64%) and concordant pain (P = .008, 61%), significantly improving the overall predictive ability of a high-intensity zone alone. A high-intensity zone went undetected in 9/11 Grade 1 disc levels with concordant pain present in 7/9. CONCLUSIONS: Consideration of enhancing annular fissures on preprocedural MR imaging substantially improves the prediction of severe/concordant pain in provocation lumbar discography.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".