Maximizing surgical resection in revision surgery for an intramedullary spinal cord tumour using DTI/tractography and direct spinal cord stimulation: A technical note
Bibliographic record
Abstract
Intramedullary spinal cord tumours are formidable lesions in spine surgery due to the high risk of postoperative deficits associated with surgical resection. Traditional MRI imaging fails to provide a 3-D rendering of the relationship between the tumour and spinal cord sensory and motor pathways; in contrast, DTI/tractography can show this relationship with the potential to reduce surgery-related morbidity. The use of intraoperative somatosensory evoked potentials (SSEP) and transcranial motor evoked potentials (MEP) have also been considered a standard of care to minimize neurologic complications of surgery. However, in situations where both baseline SSEPs and MEPs are unobtainable, D-wave and direct cord stimulation can become invaluable intra-operative tools. A 6 year old child presented with new growth from an incompletely resected thoracic intramedullary dermoid tumour. Pre-operative DTI/tractography images were obtained. During tumour resection, SSEP and MEP signals were unable to be obtained, however intraoperative monitoring of the spinal cord was undertaken with D-wave monitoring and direct cord stimulation. This multimodal monitoring technique allowed identification of the corticospinal tracts intra-operatively and allowed safe tumour resection. Post-operative MRI revealed gross total resection and the patient returned to her pre-operative neurological baseline without incurring any further deficits. We have presented a technical description of the clinical utility of DTI/tractography in surgical planning, and D-wave monitoring with direct cord stimulation as adjuncts in the resection of an intramedullary cord tumour. This is particularly useful in the scenario where TcMEP/SSEP signals cannot be obtained and in revision surgery for intramedullary spinal cord tumours.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".