Biomechanics of the Intra-Operative Lateral Decubitus Position for the Scoliotic Spine: Effect of the Pelvic Obliquity
Bibliographic record
Abstract
The intra-operative prone position used for the posterior instrumentation of scoliotic patients has been shown to reduce the spinal deformities prior to instrumentation by 37% on average. However, the effects of the lateral decubitus position used for anterior approaches and minimally invasive techniques have not been investigated. The objectives were to characterize, model and study the biomechanics of this intra-operative posture. Several clinical indices were measured on the pre- and intra-operative radiographs of six scoliotic patients. A personalized finite element mode (FEM) was developed using the pre-op 3D reconstruction, and a three-step method was developed to simulate the lateral decubitus positioning. Two additional intra-op postures, simulating different pelvic obliquities, were also tested by varying the inclination of L5. The radiographic evaluation of the lateral decubitus position showed a significant reduction of 44% of the major curve with 18 mm of apical vertebra translation. The FEM was able to reproduce the intra-op spine geometry with no significant difference with the measured values. Simulations also showed that the pelvic obliquity had different effects on the lumbar and major Cobb angles depending on the scoliotic curve type. The lateral decubitus posture reduces significantly the scoliotic curvatures prior to instrumentation, which was dependent on the pelvic obliquity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".