Excessive Fluid in the Lumbar Facet Joint as a Predictor of Radiological Outcomes After Lateral Lumbar Interbody Fusion
Bibliographic record
Abstract
Background Preoperative segmental instability maybe a predictor of postoperative outcomes when treated with lateral lumbar interbody fusion (LLIF). An abnormal collection of fluid within the facet joint has been described as a sign of segmental instability. The potential relationship between this radiological sign and its prognostic relevance for indirect decompression (ID) has not been investigated. Methods Clinical and radiologic results from patients undergoing LLIF in a single institution between 2007 and 2014 were evaluated retrospectively. Patients were divided into two groups: those presenting with excessive fluid (EF) in the facet joints on T2-MRI and those with a normal amount of facet fluid with less than 1mm, which were controls. Radiological parameters were foraminal height, disc height, Cobb angle, and lumbar lordosis. Results A total of 21 patients (43 operated levels) were evaluated pre- and postoperatively. Mean disc height, mean foraminal height, and coronal Cobb angles were statistically significantly improved after LLIF. Only the EF group showed significant improvement in radiological markers after ID; the mean disc height improved from 5.5±2 to 8.8±1mm (p=0.001), mean foraminal height improved from 16.88±3 to 20.53±3mm (p=0.002), and the mean Cobb angle improved from 27.7±16 to 14±13 (p=0.018). Conclusions Patients undergoing LLIF with the radiological findings of EF in the facet joints demonstrated significant improvement in radiological outcomes of ID. Further studies should validate these findings in larger data sets.
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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 | 0.002 |
| 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 | 0.001 | 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".