Comparison of operative and nonoperative treatment for thoracolumbar burst fractures in patients without neurological deficit: a systematic review
Bibliographic record
Abstract
OBJECT: Despite extensive published research on thoracolumbar burst fractures, controversy still surrounds which is the most appropriate treatment. The objective of this study was to evaluate the scientific literature on operative and nonoperative treatment of patients with thoracolumbar burst fractures and no neurological deficit. METHODS: In their search of the literature, the authors identified all possible relevant studies concerning thoracolumbar burst fracture without neurological deficit. Two independent observers performed study selection, methodological quality assessment, and data extraction in a blinded and objective manner for all papers identified during the search. In a synthesis of the literature, the authors obtained evidence for both operative and nonoperative treatments. CONCLUSIONS: There is a lack of evidence demonstrating the superiority of one approach over the other as measured using generic and disease-specific health-related quality of life scales. There is no scientific evidence linking posttraumatic kyphosis to clinical outcomes. The authors found that there is a strong need for improved clinical research methodology to be applied to this patient population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".