Future Advances in Spine Surgery: The AOSpine North America Perspective
Bibliographic record
Abstract
This focus issue highlights state-of-the-art techniques, equipment, and practices in the modern era of spine surgery while providing a glimpse into the next generation of patient care. A broad range of topics are presented to cover the full spectrum of the field. Degenerative diseases are discussed in a series of 3 articles on (1) pathophysiology, management, and surgical approaches to degenerative cervical myelopathy; (2) novel approaches to degenerative thoracolumbar disease (eg, interspinous process spacers, minimally invasive/endoscopic approaches); and (3) animal models and emerging therapeutics in degenerative disk disease. Also included is a unique study aiming to establish the critically important cost-benefit relationship for spine procedures with perspectives on how value is defined and how to address variability.Primary and metastatic spine oncology are reviewed with a focus on upcoming targeted biologics, subspecialized radiotherapy (eg, proton-beam, carbon-ion, stereotactic radiosurgery), genetic profiling to stratify risk, and morbidity-reducing surgical approaches (eg, minimally invasive/endoscopic resections, percutaneous instrumentation). Trauma is discussed in 2 high-quality papers on controversies in spinal trauma and neuroprotective/neuroregenerative interventions for traumatic spinal cord injury. A stimulating article on cervical, thoracolumbar, and pediatric deformity highlights the rapid evolution of deformity surgery with a look at innovative tools (eg, high-fidelity 3-dimensional reconstructions, magnetically controlled growing rods) and their impact on quality of life. Additionally, a must-read article on surgical site infections discusses key risk factors and evidence-based preventative techniques to remain aware of. Finally, cutting-edge technologies, including computer-assisted navigation, shared-control robotics, neuromodulation, novel osteobiologics, and biomaterials, are covered in detail in a series of 3 fascinating papers on the next generation of the field.Each section intends to highlight the salient literature and afford insights from multiple key thought leaders in an effort to minimize bias and provide varied perspectives. Overall, we hope this issue provides high-quality, evidence-based data relevant to trainees and practicing surgeons while also stimulating excitement about the future of spine surgery.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".