Radiological and Not Clinical Variables Guide the Surgical Plan in Patients with Glioblastoma
Bibliographic record
Abstract
Background and Purpose: The extent of resection is the most important prognostic factor in patients with glioblastoma. However, the factors influencing the decision to perform a biopsy instead of maximal resection have not been clearly established. The aim of this study was to analyze the factors associated with the intention to achieve maximal resection in glioblastoma patients. Methods: A retrospective single-center case-series analysis of patients with a new diagnosis of glioblastoma was performed. Patients were distributed into two groups: the biopsy (B) and complete resection (CR) groups. To identify factors associated with the decision to perform a B or CR, uni- and multivariate binary logistic regression analyses were performed. Cox regression analysis was also performed in the B and CR groups. Results: Ninety-nine patients with a new diagnosis of glioblastoma were included. Sixty-eight patients (68.7%) were treated with CR. Ring-enhancement and edema volume on presurgical magnetic resonance imaging were both associated with CR. Corpus callosum involvement and proximity to the internal capsule were identified as factors associated with the decision to perform a biopsy. In the multivariate analysis, edema volume (OR = 1.031; p = 0.002) and proximity to the internal capsule (OR = 0.104; p = 0.001) maintained significance and were considered independent factors. In the survival analysis, only corpus callosum involvement (HR = 2.055; p = 0.035) and MGMT status (HR = 0.484; p = 0.027) presented statistical significance in the CR group. Conclusions: The volume of edema and proximity to the internal capsule were identified as independent factors associated with the surgical decision. The radiological evaluation and not the clinical situation of the patient influences the decision to perform a biopsy or CR.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".