Treatment patterns and outcomes of patients with high-grade glioma during the COVID-19 pandemic.
Bibliographic record
Abstract
e14009 Background: During the first year of the COVID-19 pandemic there was global disruption in the provision of healthcare, causing significant pressure on hospital resources. High-grade gliomas (HGG) are rapidly progressive tumors, so patients with delays in diagnosis or treatment due to COVID-19-related disruptions might have poor outcomes. Therefore, we retrospectively evaluated the impact of the COVID-19 pandemic on treatment patterns and outcomes of patients with HGG in British Columbia (BC). Methods: A case cohort with a pathologic diagnosis of HGG (grade 4 astrocytoma and glioblastoma) treated at BC Cancer centers with radiotherapy between March 1, 2020 – March 1, 2021 (“COVID era”), and a control cohort treated between March 1, 2018 – March 1, 2019 (“pre-COVID era”) were identified. Patient demographics, tumor characteristics, treatment details, and dates of radiographic progression and death were included in the chart review. Analyses were performed with one-way ANOVA and Chi-squared tests for comparisons between eras. The Kaplan-Meier method was used to assess progression-free survival (PFS) and overall survival (OS) and differences in outcome between eras were investigated using the log-rank test. Results: 164 patients were identified: 85 in the pre-COVID era and 79 in the COVID era. There was no statistically significant baseline difference in age, sex, comorbidities, ECOG, tumor diameter, IDH mutation status, or MGMT methylation status between eras. There was also no statistically significant difference between time from symptom onset to first imaging, time from first imaging to surgery, time from surgery to oncologic consultation between eras, and time from surgery to radiotherapy. Significantly more patients were managed with biopsy relative to partial or gross total resection during the COVID era 22% (17/79) than the pre-COVID era 13% (11/85) (p = 0.04). However, radiation treatment (RT) did not differ between eras, with similar rates of conventionally fractionated RT in the pre-COVID era (87%, 74/85) and the COVID era (82%, 65/79) (p = 0.23). Use of concurrent and/or adjuvant temozolomide also was not significantly different between eras (p = 0.27 and p = 0.19, respectively). Median PFS was 7.0 months in both eras (CI95 = 5.5 – 8.5 months for pre-COVID era, CI95 = 5.8 – 8.2 months for COVID era, p = 0.3), and median OS was 13 months in the pre-COVID era (CI95 = 10.3 – 15.7 months) and 16 months in the COVID era (CI95 = 11.5 – 20.5 months), though this difference was not significant (p = 0.09). Conclusions: To our knowledge, this is the first study to assess outcomes of patients treated for HGG during the COVID-19 pandemic. We found that, despite less use of surgery in the COVID era, the outcomes of patients with HGG were not affected.
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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 | 0.001 |
| 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".