Rate and Predictors of Failure in the Conservative Management of Stable Thoracolumbar Burst Fractures: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
STUDY DESIGN: Systematic review. OBJECTIVES: Management of stable traumatic thoracolumbar burst fractures in neurologically-intact patients remains controversial. Conservative management fails in a subset of patients who require subsequent surgical fixation. The aim of this review is to (1) determine the rate of conservative management failure, and (2) analyze predictive factors at admission influencing conservative management failure. METHODS: A systematic review adhering to PRISMA guidelines was performed. Studies with data pertaining to traumatic thoracolumbar burst fractures without posterior osteoligamentous injury (e.g. AO Type A3/A4) and/or the rate and predictive factors of conservative management failure were included. Risk of bias appraisal was performed. Pooled analysis of rates of failure was performed with qualitative analysis of predictors of conservative management failure. RESULTS: 16 articles were included in this review (11 pertaining to rate of conservative management failure, 5 pertaining to predictive risk factors). Rate of failure of conservative management from a pooled analysis of 601 patients is 9.2% (95% CI: 4.5%-13.9%). Admission factors predictive of conservative management failure include age, greater initial kyphotic angle, greater initial interpedicular distance, smaller initial residual canal size, greater Load Sharing Classification (LSC) score and greater admission Visual Analog Scale (VAS) pain scores. CONCLUSION: A proportion (9.2%) of conservatively managed, neurologically-intact thoracolumbar burst fractures fail conservative management. Among other factors, age, kyphotic angle, residual canal area and interpedicular distance should be investigated in prospective studies to identify the subset of patients prone to failure of conservative management. Surgical management should be carefully considered in patients with the above risk factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".