Technical insights and implications of thoracolumbar spine and neural tissue harvesting in recently deceased organ donors: a direct anterior approach integrated into multi-organ procurement protocols
Bibliographic record
Abstract
PURPOSE: To present a novel anterior surgical technique for harvesting thoracolumbar spinal tissue from cadaveric organ donors. The approach aims to ensure biological viability of harvested spinal tissue, while maintaining full compatibility with routine multi-organ procurement protocols. By addressing the limited availability of anterior harvesting methods, this technique aims to expand opportunities for high-quality experimental and translational spine research. METHODS: A direct anterior retroperitoneal approach was employed to harvest thoracolumbar spinal columns en bloc, including the spinal cord, dorsal root ganglia (DRGs), vertebral bodies, intervertebral discs, facet joints, and posterior elements. All extractions were performed within 1–2 h following aortic cross-clamping to minimize ischemic time. The surgical procedure included targeted osteotomies to preserve structural continuity, with intraoperative radiographic imaging performed to ensure alignment and suitability for subsequent research use. Tissue samples underwent same-day processing for experimental use, including viability assays to assess the cellular health of key structures. RESULTS: This harvesting protocol was applied in over 340 cadaveric donors as part of a high-volume organ donation program. The mean harvesting time was approximately 30 min. In all cases, structural continuity of the anterior spinal column was preserved, with radiographic imaging confirming appropriate alignment and completeness of the harvested specimens. Cell viability assessments demonstrated excellent preservation of biologically active components, including viable disc and joint tissues, neural structures, and resident cell populations. The harvested tissues have been successfully used in a variety of research projects. CONCLUSION: This anterior spinal harvesting technique is a safe, efficient, and highly reproducible method that can be seamlessly integrated into standard multi-organ procurement workflows. It enables the acquisition of sterile, anatomically intact, and biologically viable spinal tissues from cadaveric organ donors without compromising donor reconstruction or surgical logistics. This approach substantially enhances the availability and quality of human spinal specimens for research and may serve as a useful model for tissue recovery in the field of spine science.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".