Recent and Emerging Advances in Spinal Deformity
Bibliographic record
Abstract
BACKGROUND: Over the last several decades, significant advances have occurred in the assessment and management of spinal deformity. OBJECTIVE: The primary focus of this narrative review is on recent advances in adult thoracic, thoracolumbar, and lumbar deformities, with additional discussions of advances in cervical deformity and pediatric deformity. METHODS: A review of recent literature was conducted. RESULTS: Advances in adult thoracic, thoracolumbar, and lumbar deformities reviewed include the growing applications of stereoradiography, development of new radiographic measures and improved understanding of radiographic alignment objectives, increasingly sophisticated tools for radiographic analysis, strategies to reduce the occurrence of common complications, and advances in minimally invasive techniques. In addition, discussion is provided on the rapidly advancing applications of predictive analytics and outcomes assessments that are intended to improve the ability to predict risk and outcomes. Advances in the rapidly evolving field of cervical deformity focus on better understanding of how cervical alignment is impacted by thoracolumbar regional alignment and global alignment and how this can affect surgical planning. Discussion is also provided on initial progress toward development of a comprehensive cervical deformity classification system. Pediatric deformity assessment has been substantially improved with low radiation-based 3-D imaging, and promising clinical outcomes data are beginning to emerge on the use of growth-friendly implants. CONCLUSION: It is ultimately through the reviewed and other recent and ongoing advances that care for patients with spinal deformity will continue to evolve, enabling better informed treatment decisions, more meaningful patient counseling, reduced complications, and achievement of desired clinical outcomes.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".