Variation in postoperative outcomes of patients with intracranial tumors: insights from a prospective international cohort study during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: This study assessed the international variation in surgical neuro-oncology practice and 30-day outcomes of patients who had surgery for an intracranial tumor during the COVID-19 pandemic. METHODS: We prospectively included adults aged ≥18 years who underwent surgery for a malignant or benign intracranial tumor across 55 international hospitals from 26 countries. Each participating hospital recorded cases for 3 consecutive months from the start of the pandemic. We categorized patients' location by World Bank income groups (high [HIC], upper-middle [UMIC], and low- and lower-middle [LLMIC]). Main outcomes were a change from routine management, SARS-CoV-2 infection, and 30-day mortality. We used a Bayesian multilevel logistic regression stratified by hospitals and adjusted for key confounders to estimate the association between income groups and mortality. RESULTS: Among 1016 patients, the number of patients in each income group was 765 (75.3%) in HIC, 142 (14.0%) in UMIC, and 109 (10.7%) in LLMIC. The management of 200 (19.8%) patients changed from usual care, most commonly delayed surgery. Within 30 days after surgery, 14 (1.4%) patients had a COVID-19 diagnosis and 39 (3.8%) patients died. In the multivariable model, LLMIC was associated with increased mortality (odds ratio 2.83, 95% credible interval 1.37-5.74) compared to HIC. CONCLUSIONS: The first wave of the pandemic had a significant impact on surgical decision-making. While the incidence of SARS-CoV-2 infection within 30 days after surgery was low, there was a disparity in mortality between countries and this warrants further examination to identify any modifiable factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".