Pedicle screw insertion in the thoracolumbar spine: comparison of 4 guidance techniques in the intact cadaveric spine
Bibliographic record
Abstract
OBJECT: Pedicle screw fixation is a mainstay of thoracolumbar stabilization. Screw insertion using anatomical landmarks and fluoroscopy is common but can be technically challenging and generally involves substantial exposure to ionizing radiation. Computerized navigation has been reported to improve accuracy but is expensive and complex. The authors undertook this study to evaluate these 3 methods in comparison with a fourth technique using standard cervical distractor screws to mark the entry point and trajectory. METHODS: Four cadaveric human spines were used for this study. After an initial CT scan, 34 pedicle screws were inserted in each intact spine from T-1 to L-5 using the following 4 screw insertion guidance techniques (1 technique per specimen): use of anatomical landmarks, use of cervical distractor screws and spot fluoroscopy, fluoroscopy-based navigation, and fluoroscopy- and CT-based navigation (using merged imaging data). Postprocedural CT and anatomical dissection were then performed to evaluate screw position for site and degree of breach. RESULTS: The cervical distractor screw method had a breach rate of 5.9% versus 29.4%, 32.4%, and 20.6% for use of anatomical landmarks, fluoroscopic navigation, and fluoroscopic-CT navigation, respectively (p < 0.05). There is also a significant association between degree of medial and distal breach and the method of screw insertion (p < 0.05). CONCLUSIONS: Cervical distractor screws as pedicle markers offer favorable insertion accuracy and reduction of radiation exposure compared with the other 3 methods used in clinical practice.
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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 | 0.002 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".