NIMG-51. THE IMPACT OF FUNCTIONAL MAGNETIC RESONANCE IMAGING ON CLINICAL OUTCOMES IN A PROPENSITY-MATCHED LOW GRADE GLIOMA COHORT
Bibliographic record
Abstract
This study aims to evaluate the impact of preoperative functional magnetic resonance imaging (fMRI) on clinical outcomes in low grade glioma (LGG) patients. In a retrospective propensity-matched cohort study, we compared LGG patients based on whether they underwent fMRI as part of preoperative assessment. Twelve LGG patients who underwent preoperative fMRI were selected, and a contemporaneous group of twelve control LGG patients who did not undergo fMRI were matched to the fMRI group based on age, sex and 1p/19q status. Functional MRI group subjects tended to have more aggressive surgeries (67% resection, 33% biopsy) than the control group (33% resection, 67% biopsy). There were no significant differences in outcomes between the groups. Time between clinical assessment and surgery tended to be longer in the fMRI group (6.3 +/- 4.2 weeks) than in the control group (2.7 +/- 2.2 weeks). Extent of resection was similar between the cohorts. Functional MRI groups subjects had lower preoperative functional status, and tended to have a greater postoperative functional status improvement than control group subjects. Mean survival was not significantly different (fMRI group five year survival 88.9%, control group five year survival 61.1%). CONCLUSIONS: We evaluated the impact of preoperative fMRI in patients with LGG in this propensity-matched cohort study. This study has not demonstrated any significant difference in outcomes between the fMRI and control groups, although there were non-significant trends for patients who underwent fMRI to undergo more aggressive surgical interventions, and have greater postoperative functional status improvement.
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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 | 0.003 |
| 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) | 0.001 | 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".