Does Extent of Resection Impact Survival in Patients Bearing Glioblastoma?
Bibliographic record
Abstract
BACKGROUND: The impact of malignant glioma resection on survival is still a matter of controversy. The lack of well-designed prospective studies as well as control of all factors in retrospective studies plays an important role in this debate. Amongst some of these uncontrolled factors, are the inclusion of different histological grades, the lack of objective methods to estimate the extent of resection and unspecified delays in post-operative imaging. METHODS: We retrospectively reviewed 126 consecutive patients with glioblastoma, operated on by the senior authors at the Centre Hospitalier Universitaire de Sherbrooke, who met the following criteria: >18 years of age, newly diagnosed glioblastoma, pre-operative magnetic resonance imaging (MRI) within 2 weeks prior to surgery, and a post-operative MRI within 72 hours after surgery. Extent of tumour resection was calculated using pre and post-operative tumour delimitation on gadolinium-enhanced T1 MRI in a volumetric analysis. RESULTS: Applying stringent specific inclusion criteria, 126 patients were retained in the analysis. The median overall survival was 271 days and the median extent of resection was 65%. Patients with more than 90% of tumour resection had a significantly better outcome, improving median survival from 225 to 519 days (P=0.006). Other factors that significantly improved survival were the use of radiotherapy, the number of regimens and type of chemotherapy used. CONCLUSION: A more aggressive approach combining maximal safe resection and use of salvage chemotherapy seems to confer a survival advantage for glioblastoma patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".