Characterizing the Morphology of Vertebral Endplate Defects: A Study of Human Cadaveric Spines Using Micro‐CT
Bibliographic record
Abstract
Introduction Back pain has been associated with vertebral endplate defects; however, findings have been inconsistent and there is substantial miscommunication surrounding the classification of different types of endplate defects observed on clinical imaging. A recent scoping review showed that 34 different terms have been used to describe various subtypes of endplate defects, many of which appear to represent the same structural abnormality. A comprehensive study of the different types of endplate structural defects is needed in order to clarify their character and prevalence. Objective This study aims to provide a thorough depiction of vertebral endplate defects classified on the basis of their morphological features and reported with respect to size, location, and prevalence in human cadaveric spines of older adults. Methods Using Micro‐CT scans, three‐dimensional reconstructed images were created of 411 endplates in the thoracolumbar (T6‐S1) spine of 19 ethanol‐phenol embalmed cadaveric specimens (9 men and 10 women, aged 62‐91). Each endplate was evaluated, and defects were identified and categorized based on their morphological characteristics. The size and location of each defect was also recorded in order to evaluate defect severity and distributions patterns. Results Seven types of endplate defects were identified, including Schmorl's Nodes, erosion, calcification, corner fracture or limbus vertebra, other fracture‐like lesions, compression, and jagged appearance. Endplate defects of >2 mm were identified on 63.5% (261) of the 411 endplates. Further, at least 2 defects were identified on 18.9% (78) of the endplates, and 3 defects were identified on 2.9% (12) of endplates. Fractures were the most common type of lesion (30.4%), followed by erosion (20.5%), and jagged surfaces (15.6%). Schmorl's Nodes constituted 8.3% of lesions. Defects were often misclassified if only viewed on sagittal images. Conclusion To the best of our knowledge, this study provides the first thorough investigation of the morphology and distribution of vertebral endplate defects using micro‐CT. The results demonstrate the presence of distinct endplate defect phenotypes with different prevalence rates. Furthermore, both defect type and size may be misrepresented if assessed only from sagittal images, as are typically used in clinical imaging. Research distinguishing endplate defect phenotypes, sizes, and distribution patterns may be critical to elucidating their etiology and role in low back pain.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".