Intraoperative and postoperative complications for repeat high-grade glioma resections with concurrent chemotherapy: patient series
Bibliographic record
Abstract
BACKGROUND: High-grade gliomas are aggressive primary brain tumors, the most common of which is glioblastoma multiforme. Despite advances in treatment, the prognosis for these patients remains poor. The most common chemotherapeutic agents used in the treatment of this pathology include temozolomide (TMZ), procarbazine, lomustine, and vincristine. It is unclear whether chemotherapy should be held during resection for high-grade gliomas, because the perioperative risk profile is not clearly defined. OBSERVATIONS: The authors report a case series of 18 surgeries to investigate the effects of concurrent TMZ and lomustine chemotherapy on surgical complications in patients undergoing repeat resection for recurrent high-grade gliomas. The authors found no postoperative infections, self-limiting postoperative complications, or excessive intraoperative blood loss and found one intraoperative complication. LESSONS: There may not be a need to pause TMZ and lomustine chemotherapy during recurrent resections for high-grade gliomas, and continuing these medications throughout the perioperative period may be appropriate. This case series suggests that patients receiving TMZ and lomustine chemotherapy who need a repeat resection for recurrent high-grade gliomas should consider remaining on their chemotherapy regimen because it has been shown in the literature to improve recurrence-free survival time.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".