SURG-76. Glowing Tumours: Implementation and utilization of 5-ALA at a Toronto academic neurosurgical center
Bibliographic record
Abstract
Abstract Gleolan™, 5-aminolevulinic acid (5-ALA) is a surgical adjunct tool used by Neurosurgeons to visualize malignant glioma intraoperatively in brain tumour surgery. Class I evidence for the effectiveness of 5-ALA suggest that 5-ALA guided resection can improve the extent of tumour removal (Wen et al., 2020). For GBM, higher extent of resection (EOR) is a significant factor in improving survival (Weller et al., 2021). 5-ALA doubles the likelihood of GTR, and has demonstrated to improve progression-free survival (PFS) (Stummer et al.,2006). Since its’ implementation at Toronto Western Hospital (TWH) in 2019, 5ALA has been utilized selectively for 60 neurosurgical-oncology patients. This presentation will detail the current evidence and benefits of 5ALA. It will outline the implementation process at Canada’s largest and leading neurosurgical center, and the role of a neuro-oncology nurse practitioner (NP) in facilitating its successful implementation. Nursing considerations and reference materials will be provided to help teams implement the use of 5ALA at their local neurosurgical center.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".