Accuracy and complications of pedicle screw insertion for lumbar and thoracolumbar fractures
Bibliographic record
Abstract
Background: The accuracy of pedicle screw placement is essential for lumbar and thoracolumbar spine fracture fixation. Purpose: The aim of the present study was to assess the accuracy of the pedicle screw placement with conventional C-arm fuoroscopy-guided in these patients. Methods: A retrospective review identified patients who underwent operative management with thoracolumbar instruments at our hospital between June 2012 and August 2013. Clinical data were acquired from medical records and final screw positions were graded based on a classification of Gertzbein and Robbins. Results: A total of 216 pedicle screws in 52 patients (34 males, mean age 32.6±5.8 years) were evaluated. They were instrumented with transpedicular posterior fixation technique within 72 hours. The follow-up time was 6.1 months (ranging from 1 to 14 months). The screws were graded A (n=43 [19.9%]), B (n=89 [41.2%]), C (n=62 [28.7%]), D (n=21 [9.7%]), and E (n=1 [0.5%]). One of the screws was revised on the second day after surgery due to screw malposition. Conclusion: Based on existing facilities, the findings showed that the pedicle instrumentation screws with transpedicular posterior fixation technique in patients with lumbar and thoracolumbar fractures can be done with acceptable complication rate. However, more advanced equipment as CT navigation (O-arm) is recommended for higher accuracy.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".