Defining the inherent stability of degenerative spondylolisthesis: a systematic review
Bibliographic record
Abstract
OBJECT A range of surgical options exists for the treatment of degenerative lumbar spondylolisthesis (DLS). The chosen technique inherently depends on the stability of the DLS. Despite a substantial body of literature dedicated to the outcome analysis of numerous DLS procedures, no consensus has been reached on defining or classifying the disorder with respect to stability or the role that instability should play in a treatment algorithm. The purpose of this study was to define grades of stability and to develop a guide for deciding on the optimal approach in surgically managing patients with DLS. METHODS The authors conducted a qualitative systematic review of clinical or biomechanical analyses evaluating the stability of and surgical outcomes for DLS for the period from 1990 to 2013. Research focused on nondegenerative forms of spondylolisthesis or spinal stenosis without associated DLS was excluded. The primary extracted results were clinical and radiographic parameters indicative of DLS instability. RESULTS The following preoperative parameters are predictors of stability in DLS: restabilization signs (disc height loss, osteophyte formation, vertebral endplate sclerosis, and ligament ossification), no disc angle change or less than 3 mm of translation on dynamic radiographs, and the absence of low-back pain. The validity and magnitude of each parameter's contribution can only be determined through appropriately powered prospective evaluation in the future. Identifying these parameters has allowed for the creation of a preliminary DLS instability classification (DSIC) scheme based on the preoperative assessment of DLS stability. CONCLUSIONS Spinal stability is an important factor to consider in the evaluation and treatment of patients with DLS. Qualitative assessment of the best available evidence revealed clinical and radiographic parameters for the creation of the DSIC, a decision aid to help surgeons develop a method of preoperative evaluation to better stratify DLS treatment options.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".