Transoral resection of axial lesions augmented by intraoperative magnetic resonance imaging
Bibliographic record
Abstract
Transoral decompression of the cervicomedullary junction may be compromised by a narrow corridor in which surgery is performed, and thus the adequacy of surgical decompression/resection may be difficult to determine. This is problematic as the presence of spinal instrumentation may obscure the accuracy of postoperative radiological assessment, or the patient may require reoperation. The authors describe three patients in whom high-field intraoperative magnetic resonance (MR) images were acquired at various stages during the transoral resection of C-2 disease that had caused craniocervical junction compression. All three patients harbored different lesions involving the cervicomedullary junction: one each of plasmacytoma and metastatic breast carcinoma involving the odontoid process and C-2 vertebral body, and basilar invagination with a Chiari I malformation. All patients presented with progressive myelopathy. Surgical planning MR imaging studies performed after the induction of anesthesia demonstrated the lesion and its relationship to the planned surgical corridor. Transoral exposure was achieved through placement of a Crockard retractor system. In one case the soft palate was divided. Interdissection MR imaging revealed that adequate decompression had been achieved in all cases. The two patients with carcinoma required placement of posterior instrumentation for stabilization. Planned suboccipital decompression and placement of instrumentation were averted in the third case as the intraoperative MR images demonstrated that excellent decompression had been achieved. Intraoperatively acquired MR images were instrumental in determining the adequacy of the decompressive surgery. In one of the three cases, examination of the images led the authors to change the planned surgical procedure. Importantly, the acquisition of intraoperative MR images did not adversely affect operating time or neurosurgical techniques, including instrumentation requirements.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".