Feasibility Study of Patient-Specific Surgical Templates for the Fixation of Pedicle Screws
Bibliographic record
Abstract
Surgery for scoliosis, as well as other posterior spinal surgeries, frequently uses pedicle screws to fix an instrumentation on the spine. Misplacement of a screw can lead to intra- and post-operative complications. The objective of this study is to design patient-specific surgical templates to guide the drilling operation. From the CT-scan of a vertebra, the optimal drilling direction and limit angles are computed from an inverse projection of the pedicle limits. The first template design uses a surface-to-surface registration method and was constructed in a CAD system by subtracting the vertebra from a rectangular prism and a cylinder with the optimal orientation. This template and the vertebra were built using rapid prototyping. The second design uses a point-to-surface registration method and has 6 adjustable screws to adjust the orientation and length of the drilling support device. A mechanism was designed to hold it in place on the spinal process. A virtual prototype was build with CATIA software. During the operation, the surgeon places either template on patient's vertebra until a perfect match is obtained before drilling. The second design seems better than the first one because it can be reused on different vertebra and is less sensible to registration errors. The next step is to build the second design and make experimental and simulations tests to evaluate the benefits of this template during a scoliosis operation.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".