P.105 Clinical impact of functional magnetic resonance imaging for pre-operative planning in patients with low grade gliomas
Bibliographic record
Abstract
Background: This study aims to evaluate the impact of pre-operative functional magnetic resonance imaging (fMRI) on low grade glioma (LGG) patients’ outcomes. Methods: In this retrospective matched cohort study (N =48) of a single surgeon’s patients, we are comparing two groups of LGG patients (WHO grade II) based on exposure to fMRI. A 1:2 propensity score match from a pool of 764 brain tumour patients was performed. Results: Within the group of 16 LGG patients who have undergone fMRI studies over a 12-year period, mean age was 40 years, and most presented with seizures (81%). Most lesions were left-sided (81%), and the lobes most commonly involved were frontal (75%) and temporal (31%). Patients underwent either craniotomy (50%), stereotactic biopsy (25%) or nonsurgically management (25%). In surgical patients, between presurgical assessment and eight week post-surgical follow-up, mean modified Rankin scale improved from 1.80±0.79 to 1.50±0.97. In our cohort, 5-year mortality was 12.5% (patients followed for a mean duration of 5.46 years). Conclusions: Data analysis is ongoing with plans to compare relevant demographics and outcomes via 1:2 propensity score matching of LGG patients who underwent fMRI against a control cohort.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".