A Prospective Randomized Comparison of CT and MRI Pre-operative Localization for Pallidotomy
Bibliographic record
Abstract
OBJECTIVE: During the pallidotomy procedure, is pre-operative localization with MRI more accurate than CT and does it result in a significant difference in surgical outcome? METHODS: Twenty-four Parkinson's Disease patients received a unilateral pallidotomy for their motor symptoms. Dyskinesia was scored pre- and six weeks postoperatively. All patients had a pre-operative CT scan and MRI to calculate the target co-ordinates. Patients were then randomly selected to proceed with either the CT or MRI coordinates. The final position for the lesion was determined with intraoperative macrostimulation and impedance measurements. The percentage improvement of dyskinesia was noted for each patient and the two groups compared by the Mann-Whitney test. The distance from the final target to the MRI and CT pre-operative co-ordinates were calculated for each patient. The mean distance for each modality was then compared by Student's t-test. The number of electrode repositionings was also recorded for each patient and the two groups compared by the nonparametric Mann-Whitney test. RESULTS: Although the MRI co-ordinates were significantly (p<0.023) closer to the final target, this did not translate into a significant reduction in electrode repositionings. There was no significant difference in the improvement in dyskinesia between the two groups. CONCLUSIONS: The pre-operative MRI co-ordinates were significantly (p=0.023) closer to the final target than those from the CT. The potential advantages and disadvantages of both imaging modalities are reviewed. There was no significant difference in surgical outcome using either MRI or CT for pre-operative localization in pallidotomy.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".