A Primer on Anatomy, Biophysics, Pathology, Imaging and Treatment of the Intervertebral Disc and the Anterior Spinal Column: The Discogenic, Intervertebral, Spinal Column (DISC) ASPN Workgroup
Bibliographic record
Abstract
Objective: As therapeutic options are rapidly expanding to treat pain of discogenic and vertebrogenic origin, it is important for the clinician to have an evidence-based understanding of the latest information regarding the normal and pathological anatomy of the intervertebral disc as well as imaging characteristics and treatment strategies. The American Society of Pain and Neuroscience (ASPN) commissioned the DISC project to inform and update the interventional spine community about anterior column pain syndromes. Methods: Using established literature review methods a clinical oversight group was convened to create an outline covering topics that will be key to meeting the objectives delineated by the ASPN scientific and guidelines leadership. This oversight group, using the created outline, selected key opinion leaders and experts to assemble the latest information on anterior column and intervertebral disc anatomy and treatment to serve as a living document that can be continuously updated to drive best practices and innovation. Results: A review of normal and pathological anatomy, a comprehensive understanding of biophysics applied to the anterior spinal column, and understanding of the current state of practice with regard to imaging, diagnostic classification systems and treatment.
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 | 0.020 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".