A novel method for making dorsal horn lesions
Bibliographic record
Abstract
OBJECTIVE: Dorsal root entry zone (DREZ) lesioning for intractable pain currently requires a multi-level laminectomy for direct access to all spinal cord segments intended to be lesioned. The hypothesis is that a silastic rubber catheter can be inserted into the dorsal horn (through a single laminectomy site) and advanced down several spinal cord segments, while staying exclusively in the dorsal horn. METHODS: A cervical laminectomy was performed in four sheep. Standard cerebrospinal fluid drainage catheters were introduced into the dorsal horn through a small incision in the DREZ. The catheters were advanced caudally along the longitudinal cord axis for a distance of 8-11 cm. Neurophysiological monitoring was done. The cord was excised from the spinal canal, fixed in formalin and cut in serial axial slices at 1 cm intervals to assess the position of the catheter within the spinal cord. RESULTS: The catheter stayed within the grey column of the spinal cord dorsal horn, along the entire length of its insertion. Electrophysiological data confirmed that dorsal horn activity was totally ablated after catheter passage in three sheep, and partially ablated in the fourth. CONCLUSION: The intrinsic architecture of the spinal cord tissue allows the predictable passage of the catheter through the column of dorsal horn grey matter. Dorsal horn lesioning can be accomplished without direct access to the cord segments selected for 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".