P.149 Saskatchewan spine pathway classification is associated with post-operative outcome and improved quality-adjusted life years following lumbosacral fusion
Bibliographic record
Abstract
Background: Low back pain (LBP) is a common cause of disability and decreased quality of life. The Saskatchewan Spine Pathway classification (SSPc) is a method for triaging patients who are candidates for surgery. Methods: Consecutive patients who underwent lumbosacral instrumented fusion for degenerative spinal pathology from Jan 1, 2012, to Sept 20, 2018, by a single surgeon at our institution were retrospectively reviewed. Patients were stratified by SSPc into 4 groups based on pain pattern. Demographic and clinical data were collected. Outcomes were compared between cohorts both for absolute values and achieving MCID. Results: 169 consecutive patients were included in our study. After stratifying by SSPc grouping, there were 61 SSPc I patients, 45 SSPc III patients, and 63 SSPc IV patients. Patients in all groups had clinical improvement following surgery. Patients classified as SSPc III had superior outcomes in ODI, EQ-5D and EQ-VAS, and were more likely to achieve the MCID for ED-5D. Multivariate analysis demonstrated that SSPc grouping is an independent predictor of final VAS back, ODI, EQ-5D, and EQ-VAS as well as achieving the MCID for EQ-5D. Conclusions: The SSPc classification is associated with outcomes following lumbosacral fusion. In particular, patients with SSPc pattern 3 had better outcomes and improved QALY.
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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) | 0.012 | 0.001 |
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".