Impact of COVID-19 Epidemic on Psycho-Oncological Distress in Neuro-Oncological Patients
Bibliographic record
Abstract
Up to 40% of neuro-oncological patients already deal with high levels of distress under conventional circumstances. Due to COVID-19, pandemic hospital visitor rules have been restricted and patients did not receive the same level of supporting caregiver network as before COVID. The aim of the present study was to analyse the impact of the COVID pandemic on the prevalence of distress, anxiety and depression in neuro-oncological patients. Patients admitted for brain tumour surgery were screened regarding distress, anxiety and depression. Furthermore, aspects of patients’ quality of life and clinical data were covered. Retrospectively available data of patients treated pre-pandemic (group A) and throughout the COVID-19 pandemic (group B) were statistically analysed using Chi-square tests and independent-sample t-tests, and regression analysis was performed to support statistical findings. Data from 110 patients were available. In all, 48 patients were included pre-COVID-19 and 62 during the COVID-19 pandemic. The authors found no significant difference between pre-COVID-19 prevalence of distress (p = 0.112), anxiety (p = 0.385) or depression (p = 0.084). Regression analyses additionally did not show any significant influence of COVID-19 on the above analysed parameter. Analyses of our cohort’s data could not underline the negative impact of COVID-19 restrictions, shortcuts of professional and remodelled caregiver support on psycho-oncological outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".