Consensus on clinical outcome measures for lumbar spinal stenosis: recommendations from the ISSLS lumbar spinal stenosis taskforce
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to determine through a Delphi process a list of outcomes measures for clinicians to use when assessing individuals with Lumbar Spinal Stenosis (LSS). METHODS: A three-phase Delphi process was conducted by the International Society for the Study of the Lumbar Spine (ISSLS) Lumbar Spinal Stenosis Taskforce, including two online surveys, two virtual meetings, and three in-person consensus meetings at the ISSLS annual conferences (2023-2025). Participants evaluated and ranked outcome measures for LSS, with final endorsement requiring > 66% agreement. RESULTS: Across three Delphi phases, 91 international subject matter experts contributed to the evaluation of 138 outcome measures for LSS. Through iterative surveys and consensus meetings, nine outcome measures across six domains -pain, self-reported and objective function, balance, sleep, and recovery- achieved final endorsement by the ISSLS Lumbar Spinal Stenosis Taskforce, each with ≥ 89% agreement. The nine outcome measures were: (1) pain/discomfort in the leg/glute during standing/walking; (2) pain/discomfort in the back during standing/walking; (3) other symptoms during standing/walking; (4) Symptom Severity Scale (Swiss Spinal Stenosis Questionnaire); (5) Oswestry Disability Index (ODI); (6) observed walking distance; (7) single leg stance; (8) ODI sleep item; and (9) Global Perceived Recovery. CONCLUSION: The study provides a consensus list of recommended outcome measures for clinicians assessing individuals with a clinical diagnosis of LSS. Although not a definitive core outcome set for routine practice, the list provides practical guidance for selecting appropriate measures for specific assessment domains. We anticipate that the list will be updated as technological advances improve our capacity to assess domains of individuals with LSS.
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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 | 0.471 | 0.499 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".