P.231 A Quantitative Degenerative Lumbar Spondylolisthesis Instability Classification (DSIC) System to Reduce Variation in Surgical Treatment
Bibliographic record
Abstract
Background: The Degenerative lumbar Spondylolisthesis Instability Classification (DSIC) system categorizes spondylolisthesis (stable, potentially unstable, unstable) based on surgeon impression. It does not contain objective criteria. Objective-1: Develop a quantitative-DSIC system from predetermined radiographic/clinical variables. Objective-2: Compare qualitative (surgeon-assigned) and quantitative (objective) DSIC Types. Objective-3: Determine proportion of patients receiving more invasive surgery than warranted based on the objective system. Methods: Patients from 8 centers were enrolled prospectively (2015–2020). Radiographic/clinical variables were collected and included/excluded from the quantitative DSIC system based on prior systematic review. Scores were converted to DSIC Types: 0-2 points (“Stable”; Type 1), 3 points (“Potentially Unstable”; Type 2), 4-5 points (“Unstable”; Type 3). Surgical procedures performed were compared to those suggested by the objective system. Results: Quantitative DSIC scores were calculated (309 patients). The score includes five variables: facet effusion, disc height, translation, disc angle, and low back pain. Quantitatively, 57% were stable, 34% potentially unstable, and 9% unstable patients. Qualitatively, 30% were stable, 53% potentially unstable, and 17% unstable patients. Surgeons assigned more instability than the objective scoring system in 42% of cases. More invasive surgery was performed in 57% of cases. Conclusions: Surgeons are more likely to categorize greater degrees of spinal instability than what is objectively scored.
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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 | 0.014 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".