Postoperative Surveillance Magnetic Resonance Imaging for Cerebellar Astrocytoma
Bibliographic record
Abstract
INTRODUCTION: Patients with low grade astrocytomas generally have good prognosis when total resection can be achieved, but surveillance neuroimaging is commonly performed to detect recurrence or progression. This study evaluated the utility and yield of such strategy for pilocytic and non-pilocytic cerebellar astrocytomas. METHODS: A 20-year retrospective review was performed of patients undergoing resection of cerebellar astrocytoma at a single institution. A negative MRI string (NMS) ratio was computed as the fraction of total follow-up period over which surveillance neuroimaging was negative for recurrence or progression. Chi-squared analysis differentiated NMS ratio by resection extent and lesion histopathology. RESULTS: Twenty-eight patients with pilocytic (n=15) and non-pilocytic (n=13) astrocytoma underwent 34 craniotomies, with total resection in 19 cases. Surveillance MRIs (n=167) among total resection patients were uniformly negative for recurrent disease at average seven years follow-up (NMS ratio = 1.0). The 43 surveillance MRIs among subtotal resection patients revealed disease progression in two patients within six months of operation (NMS ratio = 0.78, p<0.05). No differences in NMS ratio were observed between pilocytic and non-pilocytic astrocytoma subtypes. DISCUSSION: This study illustrates pediatric patients with low-grade cerebellar astrocytomas undergoing total resection may not benefit from routine surveillance neuroimaging, primarily because of low recurrence likelihood. Patients with subtotal resection may benefit from surveillance of residual disease, with further work aimed at exploring the schedule of such follow-up.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".