Percutaneous fixation of thoracolumbar vertebral fractures
Bibliographic record
Abstract
Abstract Surgical treatment of patients with thoracolumbar vertebral fracture without neurological deficit is still controversial. Management of vertebral fracture with percutaneous fixation was first reported in 2004. Advantages of percutaneous fixation are: less tissue dissection; decreased post-operative pain; decreased bleeding and operative time (depending on the steep learning curve); better screw positioning with fluoroscopy compared with an open freehand technique; and a decreased infection rate. The limitations of percutaneous fixation of vertebral fractures include increased radiation exposure to the patient and the surgeon, together with the steep learning curve for this technique. Adding a screw at the level of the fractured vertebra has the advantages of incorporating fewer motion segments with less operative time and bleeding. This also increases the axial, sagittal and torsional stiffness of the construct. Percutaneous fixation alone without grafting is sufficient for treating type A and B1 (AO classification) thoracolumbar fractures with satisfactory results concerning kyphosis reduction when compared with open instrumentation and fusion and with open fixation. Type C and B2 fractures (ligamentous injuries) should undergo fusion since the ligamentous healing is mechanically weak, increasing the risk of instability. This review offers a detailed description of percutaneous screw insertion and discusses the advantages and disadvantages. Cite this article: EFORT Open Rev 2018;3:604-613. DOI: 10.1302/2058-5241.3.170026.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".