The Effect of After-Hours Resection on the Outcomes in Patients with High-Grade Gliomas
Bibliographic record
Abstract
ABSTRACT Objective: 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 glioma requiring emergent surgery between January 1, 2021, and March 31, 2023. After hours was defined as surgical resection on the weekend and/or evening (>50% of surgical time between 1630 and 0659). These patients were matched to patients undergoing resection during regular working hours. Groups were compared on the basis of the extent of resection, postoperative complications and 6-month mortality rate. 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 the rate of intraoperative complications, postoperative complications, reoperation and death at 6 months between the groups (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 has similar outcomes to regular-hours surgery for lesions not requiring specialized functional mapping.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".