P.113 Impact of 5-ALA on rates of complete high grade glioma resection: a Canadian perspective
Bibliographic record
Abstract
Background: The relationship between glioblastoma extent of surgical resection (EoR) and survival is well documented.1-3 The advent of 5-aminolevulinic acid (5-ALA), a tissue selective fluorophore, has led to increased rates of gross total tumour resection.4-6 Since 5-ALA received approval for use in Canada in 2020, no Canadian centres have examined its impact on rates of complete resection (CR) for newly diagnosed high grade glioma (HGG) patients. Methods: This study evaluates the difference in EoR class7, for newly diagnosed HGG. Fifty-one consecutive patients underwent awake craniotomy with white light illumination (WLS) while 45 consecutive HGG patients were operated with fluorescence guidance (FGS). Analysis of EoR class was blinded and performed by 2 independent reviewers with a third adjudicator available for discrepancies. Residual tumour volumes were quantified by segmentation of postoperative 1mm slice MRI. Results: The FGS group was found to have: 80% complete resection (CR), 11% near-total resection (NTR), and 9% subtotal resection (STR). This compared favourably to the WLS respective rates of 67%, 6%, and 28%. Conclusions: For awake craniotomy protocol, the odds of complete resection were higher in the FGS group, compared to the WLS group (OR = 2; 95% CI 1.06, 2.93).
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".