P.173 Evaluating instability in Degenerative Lumbar Spondylolisthesis: objective variables versus surgeon impressions
Bibliographic record
Abstract
Background: The qualitative Degenerative Spondylolisthesis Instability Classification (DSIC) system defines pre-operative instability associated with degenerative lumbar spondylolisthesis (DLS) and facilitates surgical technique selection. Objectives: (1) propose a quantitative DSIC system; (2) compare objective measures to surgeon impressions of DLS-related instability. Methods: We conducted a multi-center prospective study of 408 adult patients undergoing surgery for DLS. Variables included in the quantitative classification were assigned point-values based on evidence quality. Scores were converted to DSIC Types: 0-2 points (“Stable”; Type I), 3 points (“Potentially Unstable”; Type II), 4-5 points (“Unstable”; Type III). Surgeons documented impressions of instability using the qualitative DSIC system. Results: Five variables were included in the quantitative DSIC: presence of facet effusion, preservation of disc height (<6.5mm), translation (>4mm), kyphotic or neutral disc angle in flexion, and presence of low back pain (LBP) (>5/10 intensity). Surgeons categorized higher degrees of instability than the preliminary quantitative DSIC system, in 130 patients (42%) (P < 0.001). Compared to procedures suggested by the quantitative DSIC system, more extensive surgical procedures were performed in 150 patients (57%) (P < 0.001). Conclusions: A quantitative DSIC system allowed DLS-related stability to be scored and categorized. Patients potentially received more extensive surgery than warranted based on quantitative assessments of stability.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".