Neuro-oncology management during the COVID-19 pandemic with a focus on WHO grades III and IV gliomas
Bibliographic record
Abstract
BACKGROUND: Because of the increased risk in cancer patients of developing complications caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), physicians have to balance the competing risks of the negative impact of the pandemic and the primary tumor. In this consensus statement, an international group of experts present mitigation strategies and treatment guidance for patients suffering from high grade gliomas (HGG) during the coronavirus disease 2019 (COVID-19) pandemic. METHOD / RESULTS: 16 international experts in the treatment of HGG contributed to this consensus-based practice recommendation including neuro-oncologists, neurosurgeons, radiation -oncologists and a medical physicist. Generally, treatment of neuro-oncological patients cannot be significantly delayed and initiating therapy should not be outweighed by COVID-19. We present detailed interdisciplinary treatment strategies for molecular subgroups in two pandemic scenarios, a scale-up phase and a crisis phase. CONCLUSION: This practice recommendation presents a pragmatic framework and consensus-based mitigation strategies for the treatment of HGG patients during the SARS-CoV-2 pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".