Evaluating the Effect of Preoperative Symptom Duration on Patient-Reported Outcomes Following Anterior Lumber Interbody Fusion
Bibliographic record
Abstract
INTRODUCTION: Anterior lumbar interbody fusion (ALIF) continues to grow as a cornerstone treatment option for degenerative lumbar spine pathologies. The relationship between preoperative symptom duration and outcomes following ALIF is not well established. The purpose of this study is to assess the effect of preoperative symptom duration on postoperative functional and pain outcomes following ALIF. METHODS: This was a retrospective cohort study including patients who underwent primary one- or two-level ALIF at a single academic institution between 2017 and 2023. Patients were grouped into shorter (<12 months) and prolonged (≥12 months) cohorts based on preoperative symptom duration. Outcomes included Oswestry disability index (ODI), visual analog scale Back and Leg, and 12-Item Short Form Survey (SF-12) physical component score. Change in patient-reported outcome measure scores and minimal clinically important difference (MCID) rates were calculated. Analyses were conducted on the early (<6-months) and late (≥6 months) postoperative periods. RESULTS: A total of 145 patients (52.8 ± 12.8 years, 55.5% female) were included. In the early postoperative period, the shorter preoperative symptom duration cohort experienced markedly greater improvement from preoperative patient-reported outcome measure scores compared with the prolonged symptom duration cohort for ODI, VAS-Back, and VAS-Leg. The shorter symptom duration cohort achieved MCID in the early and late postoperative periods at a markedly higher rate for ODI (early: 80.5% vs. 25.0%, P < 0.001; late: 88.9% vs. 64.8%, P = 0.008). On multivariable regression analysis, prolonged preoperative symptom duration (≥12 months; odds ratio: 3.69, P = 0.006) was identified as an independent predictor for failure to achieve MCID for ODI at latest follow-up. CONCLUSION: Our study demonstrates improved clinical outcomes for patients with shorter preoperative symptom duration undergoing ALIF, suggesting that delayed surgical intervention may result in worse outcomes and greater postoperative disability. These findings may help inform the approach to counseling patients on postoperative expectations and outcomes based on their preoperative symptom duration.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".