P.136 Intraoperative 5-ALA fluorescence-guided resection of high-grade glioma leads to greater extent of resection with better outcomes: a systematic review
Bibliographic record
Abstract
Background: 5-aminolevulinic acid (5-ALA) enhances intraoperative high grade glioma (HGG) tissue visualization. Despite promising randomized clinical trial data suggesting survival benefit for 5-ALA-guided HGG surgery, patient outcome efficacy is not universally accepted. Methods: We performed a systematic review of the literature to evaluate whether there is a beneficial effect upon survival and extent of resection from the utilization of 5-ALA in HGG surgery. Literature regarding 5-ALA usage in HGG surgery was reviewed according to PRISMA guidelines. Results: 3,756 published studies were screened, 536 evaluated, and 45 included. Of studies that directly compared the use of 5-ALA to white light (28.9%), 5-ALA lead to a better progression-free survival (PFS) and overall survival (OS) in 88.4 and 67.5% of patients, respectively. 42.2% demonstrated that 5-ALA use was associated with less post-op neurological deficits, whereas 23.3% of studies showed that surgeries using 5-ALA lead to more deficits. 34.5% demonstrated no difference between 5-ALA and without. Conclusions: 5-ALA was found to be associated with a greater extent of resection and longer OS and PFS in HGG surgeries. Postop neurologic deficit rates were inconclusive when comparing 5-ALA groups to white light groups. 5-ALA is a useful surgical adjunct for HGG resection with preserved patient safety.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".