Intra-operative Tracking of the Trunk during Surgical Correction of Scoliosis: A Feasibility Study
Bibliographic record
Abstract
objective: The purpose of this study was to evaluate the feasibility of a technique for intra-operative tracking of the trunk during scoliosis surgery.Materials and Methods: Eleven magnetic sensors placed on specifie anatomical landmarks are used to compute 11 geometrie indices of the trunk. This technique was assessed on a cohort of 40 subjects (19 normal, 21 scoliotic) using an experimental set-up simulating the position of patients during scoliosis surgery.Results: The indices varied less than 2 mm and 1d` when breathing (except for ehest AP diameter), and less than 1 mm and 1d` after transient displacement from the initial positioning of the subjects. No significant changes were observed for most of the indices between two acquisition sessions. Comparison between normal and scoliotic subjects demonstrated that the trunk geometry is more influenced by the positioning of each subject on the operating table than by the magnitude of the spinal deformity.Conclusion: The proposed technique will allow intra-operative tracking of the trunk and enable the surgeon to optimize the correction of both spinal and trunk deformities. The technique can also be used to evaluate the adequacy of patient positioning on the operating table, and to obtain a complete follow-up of patients in pre-, intra-, and post-surgical conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".