Adverse Events and Treatment Failure in Patients With Thoracolumbar Burst Fractures Without Neurological Deficit: A Sub Analysis From Prospective Multicentric Study
Bibliographic record
Abstract
Study design Prospective multicentric study. Objective Thoracolumbar fractures without neurologic deficit are challenging situations in terms of treatment decision making. We aimed to analyze the occurrence of adverse events (AEs) after surgical and nonsurgical treatment and its impact on functional outcomes. Methods 198 patients from a prospective multicentric database were included. The occurrence of adverse events and treatment failure within 2 years of follow up were recorded. ODI was compared between patients with and without adverse events at six months, 1 year and 2 years follow up. Multivariable regression analysis was conducted to assess the association between post-treatment adverse events and ODI at 1-year follow-up. Results 46 adverse events were recorded (23.2%). Higher categories of the Charlson Comorbidity Index (CCI) ( P = 0.006), unemployment or retirement ( P = 0.027), and current smoking ( P = 0.008) were significantly associated with the occurrence of adverse events whereas no significant differences were observed in terms of treatment decision (conservative vs surgical). ODI values were significantly higher in patients with adverse events at the 6-month and 1-year follow-up visits without significant differences at 2 years follow up. Treatment failure was observed in only 5 patients. Conclusion We found association between the occurrence of AE and higher ODI at 6-months and one-year follow up. Additionally, a higher CCI and smoking status were associated with higher likelihood to develop adverse events in our cohort.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".