P.086 The effect of after-hours surgical resection on the outcomes in patients with high grade gliomas
Bibliographic record
Abstract
Background: The “weekend effect” is the finding that patients presenting for medical care outside of regular working hours tend to have worse outcomes. There is a paucity of literature in the neuro-oncology space exploring this effect. We investigated the extent of resection and complication rates in patients undergoing after-hours high grade glioma resection. Methods: A retrospective review was conducted on patients with high-grade gliomas requiring emergent surgery between January 2021 to March 2023. After-hours was defined as surgical resection on the weekend and/or evening. These patients were matched to patients undergoing resection during regular working hours. Results: A total of 38 patients were included in this study (19 after-hours, 19 regular hours). There was no significant difference in age, sex, tumor grade, and tumor size between the two groups (all p>0.05). There was no significant difference in the extent of resection between the groups (p=0.7442). There was no significant difference in complications rates, reoperation rates, and death at 6 months (all p>0.05). Estimated blood loss was significantly higher in the regular hours group (p=0.0278). There was no significant difference in the total operative time (p=0.0643) and length of stay (p=0.0601). Conclusions: After-hours high grade glioma surgery is not associated with increased morbidity or mortality.
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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 | 0.003 |
| 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) | 0.006 | 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".