The Cost-Effectiveness of 5-ALA in High-Grade Glioma Surgery: A Quality-Based Systematic Review
Bibliographic record
Abstract
BACKGROUND: High-grade gliomas (HGGs) are aggressive tumors that inevitably recur due to their diffusely infiltrative nature. Intraoperative adjuncts such as 5-aminolevulinic acid (5-ALA) have shown promise in increasing extent of resection. As the prospect of increased use of 5-ALA rises, a systematic overview of the health economics of this adjunct is critical. METHODS: Medline, EMBASE, Centre for Reviews and Dissemination, EconPapers, and Cochrane databases were searched for keywords relating to glioma, cost-effectiveness, and 5-ALA. Primary studies reporting on the health economics or cost-effectiveness of 5-ALA compared to white light surgery in HGG were included. Quality was assessed using the British Medical Journal guidelines. RESULTS: Three studies were identified. All were European and conducted from the perspective of national healthcare systems. Two studies demonstrated the cost-utility of 5-ALA compared to white light (C$12,817 and C$13,508/quality-adjusted life-years (QALYs)). One assessed the cost-utility per gross total resection (C$6,813). Both these values were below the national cost-effectiveness thresholds for each respective study. The third study demonstrated no significant difference in cost of 5-ALA in glioblastoma resection (C$14,732) compared to prior to its routine use (C$15,936). The quality of these studies ranged from moderate to average. None of these studies considered patient perspective or indirect costs in their analysis. CONCLUSIONS: Growing evidence exists examining the health economic benefit of 5-ALA as an intraoperative adjunct for HGG resection. Additional studies within the Canadian context using 5-ALA, specifically incorporating patient and societal perspectives into the cost-utility analyses, are necessary to solidify this line of evidence.
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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 | 0.015 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.016 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".