3D Sagittal Parameters Can Guide the Indications for Anterior Release in Thoracic AIS ≥70°
Bibliographic record
Abstract
Study Design Retrospective, multicenter. Objectives This study aims to evaluate the immediate postoperative effect of, and define indications for, an anterior release (discectomy) in large AIS curves utilizing 3D deformity analysis. Methods A multicenter registry was queried for AIS patients with main thoracic curves ≥70° treated with either anterior/posterior (AP) or posterior-only surgery and biplanar stereoradiographic pre-operative and first-erect (FE) postoperative images. Standard 2D radiographic and 3D parameters were analyzed using custom MATLAB software. 3D thoracic kyphosis (3DTK) was calculated by removing the error induced by axial rotation and coronal deformity. Results 109 patients were included, 21 AP and 88 posterior-only. The AP group had larger (89° vs 76°, P < .001), less flexible (9% vs 21%, P = .001) curves, though greater percent correction (79% vs 71%, P = .003), producing similar postoperative curve magnitude (19° vs 22°, P = 0.1). The AP group had less preoperative 3DTK (−15° vs −3°, P < .001), though similar postoperative 3DTK (24° vs 20°, P = .1), nearly double the improvement (39° vs 23°, P < .001). No cases with preoperative 3DTK < −18° achieved postoperative 3DTK >25° without anterior release. Segmental data of each motion segment demonstrated anterior release led to greater change in the coronal ( P < .001) and sagittal ( P = .003) planes, though not axial rotation of the apical vertebra ( P = .157). Conclusion In a cohort of AIS patients with thoracic curve magnitude >70°, 3D analysis comparing anterior/posterior vs posterior-only approach demonstrated improved correction in the coronal and sagittal, but not the axial plane. If 3DTK preop was <−18° only anterior release patients achieved postoperative 3DTK >25°. Level of Evidence III.
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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".