Complete Versus Incomplete Surgical Resection in Intramedullary Astrocytoma: Systematic Review with Individual Patient Data Meta-Analysis
Bibliographic record
Abstract
Study Design Systematic review Background: Considering the infiltrative nature of intramedullary astrocytoma, the goal of surgery is to have a better patient related outcome. Objective To compare the overall survival (OS) and neurologic outcomes of complete vs incomplete surgical resection for patients with intramedullary astrocytoma. Methods A comprehensive search of MEDLINE, CENTRAL and EMBASE was conducted by two independent reviewers. Individual patient data (IPD) analysis and multivariate Cox Proportional Hazard Model was developed to measure the effect of surgical strategies on OS, post-operative neurological improvement (PNI), and neurological improvement in the last follow up (FNI). Results We included 1079 patients from 35 studies. Individual patient data of 228 patients (13 articles) was incorporated into the integrative IPD analysis. Kaplan-Meier survival analysis showed complete resection (CR) significantly improved OS in comparison with the incomplete resection (IR) (log-rank test, P = .004). In the multivariate IPD analysis, three prognostic factors had significant effect on the OS: (1) Extent of Resection, (2) pathology grade, and (3) adjuvant therapy. We observed an upward trend in the popularity of chemotherapy, but CR, IR, and radiotherapy had relatively stable trends during three decades. Conclusion Our study shows that CR can improve OS when compared to IR. Patients with spinal cord astrocytoma undergoing CR had similar PNI and FNI compared to IR. Therefore, CR should be the primary goal of surgery, but intraoperative decisions on the extent of resection should be relied on to prevent neurologic adverse events. Due to significant effect of adjuvant therapy on OS, PNI and FNI, it could be considered as the routine treatment strategy for spinal cord astrocytoma.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.031 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".