P.033 Biopsy versus subtotal versus gross total resection in patients with low-grade glioma: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: The role of extent of surgical resection (EOR) on clinical outcomes in patients with low-grade glioma requires further examination. Methods: We systematically searched MEDLINE, Embase, and the Cochrane Library for studies published between January 1, 1990 and January 5, 2018 on predefined patient outcomes regarding different EOR of low-grade glioma. Results: Our literature search yielded 60 studies including 13,289 patients. Pooled estimates of overall survival showed an increase from 3.79 years (95% CI, 2.37–5.22) in the biopsy group to 6.68 years (95% CI, 4.19–9.16) in STR to 10.65 years (95% CI, 6.78–14.52) in GTR. When compared to STR, GTR prolonged progression-free survival by 2.08 years (95% CI, 0.26–3.89; P=0.025). Pooled estimates of seizure control showed an improvement from 47.8% (95% CI, 26.7–69.6) with biopsy to 54.2% (95% CI, 48.7–59.6) with STR to 81.0% (95% CI, 74.6–86.2) with GTR. Compared to STR, GTR delayed malignant transformation (RR, 0.43; 95% CI, 0.20–0.93; P=0.032), without increasing postoperative mortality (RR, 0.38; 95% CI, 0.07–1.97; P=0.250) or morbidity (RR, 1.22; 95% CI, 0.65–2.28; P=0.540). Conclusions: Among patients with low grade gliomas, higher degrees of safe EOR, were associated with longer overall and progression-free survival, better seizure control, and delayed malignant transformation, without increased mortality or morbidity.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.044 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".