Extent of surgical resection of high-grade glioma among the elderly
Bibliographic record
Abstract
Zou et al express 2 concerns in their letter about our recent meta-analysis.1 First, they believe that some included articles may have used biopsy for diagnostic purposes and accordingly should not be pooled in the analyses. Their doubt that biopsy might have been used for diagnosis is the exact definition we used (“the use of surgical procedures exclusively for pathological diagnostic purposes”) in the meta-analysis and study protocol.1 Researchers2,3 compare biopsy to resection for 2 main reasons: (i) biopsy cannot be achieved without resecting a minimal amount of tumor tissue and (ii) by examining biopsy as a control group versus patients undergoing resection, the value of extent of surgical resection can be defined. The second concern by Zou et al is that outcomes among elderly persons with high-grade gliomas (HGGs) depend on multiple factors, which is a reiteration of what we discussed in our study. It is almost impossible in a meta-analysis and extremely difficult in a randomized trial to account for similar characteristics between examined groups related to patients’ age, gender, and underlying comorbidities, tumor size; location, histology, and genetics; preoperative functional status, type, dose, and duration of chemotherapy; and type, dose, and duration of radiotherapy. Our main goal was to examine the value of extent of surgical resection, not to recommend gross total resection for all patients. Hence, our conclusion that improved outcomes were associated with higher extent of resection was confounded by what we reported in the abstract and manuscript “if considered in conjunction with known established safety measures when managing elderly patients harboring HGGs.”1 We deliberately and precisely used “associated” in describing the relation between improved outcomes and increasing extent of surgical resection.1 This implies that extent of resection is a significant factor—not, however, the only factor. A recommendation of gross total resection for all patients cannot be made and accordingly was avoided in our systematic review. Not resecting HGGs at the expense of safety and functional outcomes among the elderly is a well-known established practice by neurosurgeons globally. The main question we aimed to answer through the first meta-analysis of the literature and a large cohort among the elderly was about selecting the optimal extent of resection when all extents can be achieved within the safety limits. This should not be interpreted in isolation of the other well-known factors. None declared. Conflict of interest statement. None declared.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.013 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".