COVD-06. BRAIN TUMOURS AND COVID-19: THE PATIENT AND CAREGIVER EXPERIENCE
Bibliographic record
Abstract
Abstract BACKGROUND Since the COVID-19 pandemic, thousands of medical procedures and appointments have been cancelled or delayed. The long-term effects of these drastic measures directly on brain tumour patients and their caregivers are unknown. The purpose of this study is to better understand how COVID-19 has affected this vulnerable population on a global scale. METHODS An online 79-question survey was developed by the IBTA, in conjunction with the SNO COVID-19 Task Force. The survey was sent to over 120 brain tumour charities and not-for-profits worldwide and disseminated to brain tumour patients and caregivers. All responses were subdivided by patient vs caregiver and by geographical region. RESULTS In total, 1989 participants completed the survey from 33 countries, including 1459 patients and 530 caregivers. There were no significant differences in COVID-19 testing rates (p = 0.662) or the number of positive cases for brain tumour patients between regions (p = 0.1068). Caregivers were significantly more anxious than patients (p = < 0.0001). Patients from the Americas were most likely to have lost their jobs due to the pandemic, practiced self-isolation, and received telehealth services (p = < 0.0001). Patients from Europe experienced the most treatment delays (p = 0.0031). Healthcare providers were ranked as the most trusted source of information. CONCLUSIONS As a result of COVID-19, brain tumour patients and caregivers have experienced significant stress and anxiety. Healthcare professionals, brain tumour charities, and not-for-profits must ensure that they continue to provide accessible, high-quality care, information, and support in the age of COVID-19.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".