Bibliographic record
Abstract
Compared to a decade ago, the field of spinal surgery has become more technologically driven than ever. While such change ultimately helps to improve the health care we deliver, it can also leave behind those of us who have become comfortable in the routines in which we practice. The craniocervical junction is one of the most complex and challenging environments for those who work in and around the nervous system, bridging both cranial and spinal pathology alike. This issue of Seminars in Neurosurgery is directed towards providing a comprehensive overview of what we felt to be some of the more important and common problems encountered in this region. The contributors have been selected not only because of their expertise and their contributions in this area of surgery, but because of their vision for the future. They represent all corners of this continent. In my opinion, these authors are the ``cream'' of our most recent generation of super-specialists-individuals who will be leading this field into its next period of evolution and setting the standards for our grandchildren to follow. Dr. Crawford and I have attempted to present a brief overview of biomechanical considerations of the craniocervical junction. Dr. Sahjpaul shares with us some of the details specific to congenital anomalies, including a concise and pertinent review of embryology and a look at achondroplasia and Down Syndrome. Drs. Javahery, Vanni, and Levi continue with an informative examination of the Chiari malformations and syringomyelia. Drs. Tuli and Woodard insightfully dissect one of the most common disease processes to ravage the craniovertebral junction: rheumatoid arthritis. Progress in the management of neoplasia is apparent from the expert review of Drs. York and Gokaslan. Drs. du Plessis, Hanna, Lauryssen, Austin, and Alexander meticulously approach trauma level by level, from occiput to C2. Finally, Drs. Duggal, Campos, Cortez, Paramore, and Toselli finish off with a practiced surgical eye towards approaches, instrumentation systems, and complications. In all articles, text is concise and figures pertinent to help the reader navigate with minimal difficulty. It is our hope that this issue of Seminars in Neurosurgery will provide the surgeon with perhaps a few hours of enjoyable yet educational reading time. I would like to personally acknowledge and thank each of the authors for the time they have invested in this publication. Their combined contributions have made this an outstanding contribution to publications in neurological and spinal 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.045 | 0.020 |
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".