Lumbar disc rehydration after dynamic stabilization: A systematic review
Bibliographic record
Abstract
BACKGROUND: Interbody fusion combined with posterior screw fixation is a traditional method used for treating lumbar degenerative disease (LDD). But in recent years, there have been more and more reports about its complications. Dynamic Stabilization Systems (DSS) are another method for the treatment of LDD, but the clinical results are still inconclusive. The objective of this study is to review, analyze, and discuss the probability of disc rehydration by DSS designed for LDD by systematically reviewing previous relevant studies. METHODS: The Pubmed, Web of Science, and Embase databases were searched using keywords for articles published before June 2022. The following keywords were used: rehydration, rehydrated, lumbar, spine, disc, spinal, degenerative disc disease, degenerative spine disease, vertebrae, vertebral column, thoracolumbar, and lumbosacral. The included studies were printed in English. Two independent investigators compiled all data. For the quality assessment, the Newcastle-Ottawa Scale was used to evaluate case-control studies, while the Joanna Briggs Institute critical appraisal checklist was used to evaluate the case series studies. RESULTS: This systematic review included 7 studies comprised of 5 case series and 2 case-control studies. Seven articles involving 199 cases were enrolled for the data extraction. Of the 199 cases, 55 cases observed rehydration, as evaluated by Pfrimann grading on magnetic resonance imaging. The rehydration rate was 27.6% (55/199). DSS can provide positive clinical outcomes. Both visual analog scale and Oswestry Dysfunctional Index scores were significantly improved at the final follow-up. CONCLUSION: DSS may promote disc rehydration and delay the development of LDD to some extent. Mechanical stretch may play an important role in the progress of intervertebral disc rehydration. It provides important evidence for the clinical application of DSS.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".