Positioning Thoracic Pedicle Screw Entry Point Using a New Landmark
Bibliographic record
Abstract
STUDY DESIGN: A novel method to identify the entry point. OBJECTIVE: To quantify the position of thoracic pedicle screw entry points on the lamina at various segments of the thoracic vertebrae in normal subjects and patients with adolescent idiopathic scoliosis and propose a new technique to select entry points using a new landmark. SUMMARY OF BACKGROUND DATA: Thoracic pedicle screws have been widely used in thoracic surgery, and the placement of pedicle screws has been studied extensively. However, there are only qualitative studies on selecting the entry point, and no study has quantified the position of entry points. METHODS: A retrospective study using 3-dimensional computed tomographic reconstruction techniques were used to study the morphology of thoracic vertebrae in 110 adolescents (56 cases of adolescent idiopathic scoliosis and 54 normal subjects). A quantitative area was used to select the entry point. Thoracic pedicle screw entry point was determined using the new landmark as reference and thoracic pedicle screws were placed in 21 patients. Postoperative computed tomographic scanning was performed to assess the safety and effectiveness of this entry point selection technique. RESULTS: We determined that the accuracy of pedicle screw placing after positioning entry point using the quantitative area was significantly superior to that after positioning entry point using the traditional method (P < 0.05). CONCLUSION: The new technique quantifies the position of each thoracic pedicle screw entry point and it is convenient, easy to operate, and has relatively high accuracy of screw placement. This positioning technique can provide safe and accurate clinical guidance for selecting thoracic pedicle screw entry point.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".