P.102 Weighing the risks and benefits of perioperative steroids in the surgical treatment of malignant brain tumours
Bibliographic record
Abstract
Background: Steroids are widely used in medicine because of their anti-inflammatory and immunosuppressive properties; however, they have numerous adverse effects. In neuro-oncology, dexamethasone is the first-line treatment for vasogenic edema caused by malignant brain tumours. This retrospective chart review investigated the risks and benefits of perioperative steroids in the surgical treatment of malignant brain tumours. Methods: All patients (age ≥ 18 years) who underwent a craniotomy for the treatment of a malignant brain tumour at Windsor Regional Hospital between 2012 and 2020 were assessed for eligibility for this retrospective study. Baseline patient characteristics, cumulative perioperative steroid dose, and postoperative outcomes were recorded from electronic medical records (n = 362). Statistical analysis was performed using SPSS. Results: Patients who received a higher cumulative perioperative steroid dose (≥ 80 mg) had a significantly higher rate of postoperative complications compared to those who received a lower dose. These included wound dehiscence, postoperative brain edema on imaging, unplanned return to the operating room, and readmission within 30 days. Conclusions: Steroids are important medications in neuro-oncology, but they are not without potential complications. The findings of this study highlight the need for careful consideration when using steroids in patients undergoing surgical treatment of a malignant brain tumour.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".