Percutaneous Dorsal Instrumentation of Vertebral Burst Fractures: Value of Additional Percutaneous Intravertebral Reposition—Cadaver Study
Bibliographic record
Abstract
PURPOSE: The treatment of vertebral burst fractures is still controversial. The aim of the study is to evaluate the purpose of additional percutaneous intravertebral reduction when combined with dorsal instrumentation. METHODS: In this biomechanical cadaver study twenty-eight spine segments (T11-L3) were used (male donors, mean age 64.9 ± 6.5 years). Burst fractures of L1 were generated using a standardised protocol. After fracture all spines were allocated to four similar groups and randomised according to surgical techniques (posterior instrumentation; posterior instrumentation + intravertebral reduction device + cement augmentation; posterior instrumentation + intravertebral reduction device without cement; and intravertebral reduction device + cement augmentation). After treatment, 100000 cycles (100-600 N, 3 Hz) were applied using a servohydraulic loading frame. RESULTS: Overall anatomical restoration was better in all groups where the intravertebral reduction device was used (p < 0.05). In particular, it was possible to restore central endplates (p > 0.05). All techniques decreased narrowing of the spinal canal. After loading, clearance could be maintained in all groups fitted with the intravertebral reduction device. Narrowing increased in the group treated with dorsal instrumentation. CONCLUSIONS: For height and anatomical restoration, the combination of an intravertebral reduction device with dorsal instrumentation showed significantly better results than sole dorsal instrumentation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".