SURG-22. QUALITY BASED ASSESSMENT OF THE COST-EFFECTIVENESS OF 5-ALA IN HIGH GRADE GLIOMA SURGERY: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
High grade gliomas (HGG) are the most common primary malignant brain tumors in adults. While the infiltrative nature of HGG makes safe maximal resections challenging, studies on the use of adjuncts such as 5-ALA have shown much promise. The accumulating clinical evidence has led to approval of 5-ALA for clinical practice in Europe but not in North America. Prior to integration into routine practice, data on the health economic impact of novel agents are considered by regulatory health agencies. With respect to 5-ALA, this evidence is limited and requires further investigation. A systematic review and synthesis of the state of evidence on health economic assessment of 5-ALA in HGG surgery was conducted. Medline, EMBASE, CRD, EconPapers and Cochrane databases were searched for keywords related to glioma, cost-effectiveness and 5-ALA. Three primary studies were identified (1 Portuguese, 1 Spanish and 1 French). One measured incremental cost-effectiveness ratio (ICER) of 5-ALA per gained Quality Adjusted Life Year (QALY) compared to white light surgery. Another assessed cost-effectiveness based on incremental cost per QALY and per complete resection. The first study reported ICER of €9100/ QALY while the second reported €9021/QALY. Both studies reported that 5-ALA was well below their respective national cost-effectiveness thresholds. The third study compared glioblastoma (GBM) surgical costs during a period before 5-ALA’s introduction and after it started being widely used. The difference in average costs of GBM surgery during these periods, €9353 compared to €10,118, was not significant. None of these studies considered patient’s perspective and indirect costs in their analysis. While clinical evidence in favor of 5-ALA as intraoperative adjunct is accumulating, health economic data is limited and of low quality. Prospective high quality evaluations on cost-effectiveness of 5-ALA are necessary prior to its regulatory approval and routine clinical use in North America.
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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 | 0.016 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.022 |
| Bibliometrics | 0.015 | 0.014 |
| 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) | 0.011 | 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".