Beneficial Effects of Preoperative Exercise on the Outcomes of Lumbar Fusion Spinal Surgery
Bibliographic record
Abstract
Purpose: To determine whether there was an association between self-reported preoperative exercise and postoperative outcomes after lumbar fusion spinal surgery. Method: We performed a retrospective multivariable analysis of the prospective Canadian Spine Outcomes and Research Network (CSORN) database of 2,203 patients who had elective single-level lumbar fusion spinal surgeries. We compared adverse events and hospital length of stay between patients who reported regular exercise (twice or more per week) prior to surgery (“Regular Exercise”) to those exercising infrequently (once or less per week) (“Infrequent Exercise”) or those who did no exercise (“No Exercise”). For all final analyses, we compared the Regular Exercise group to the combined Infrequent Exercise or No Exercise group. Results: After making adjustments for known confounding factors, we demonstrated that patients in the Regular Exercise group had fewer adverse events (adjusted odds ratio 0.72; 95% CI: 0.57, 0.91; p = 0.006) and significantly shorter lengths of stay (adjusted mean 2.2 vs. 2.5 d, p = 0.029) than the combined Infrequent Exercise or No Exercise group. Conclusions: Patients who exercised regularly twice or more per week prior to surgery had fewer postoperative adverse events and significantly shorter hospital lengths of stay compared to patients that exercised infrequently or did no exercise. Further study is required to determine effectiveness of a targeted prehabilitation programme.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".