The Impact of the COVID-19 Pandemic on the Number of Cancer Patients and Radiotherapy Procedures in the Warmia and Masuria Voivodeship
Bibliographic record
Abstract
(1) Background: It was suspected that the COVID-19 pandemic would negatively affect health care, including cancer treatment. The aim of the study was to assess the impact of the COVID-19 pandemic on the number of radiotherapy procedures and patients treated with radical and palliative radiotherapy in Poland. (2) Methods: The study was carried out in Warmia and Masuria voivodeship. The number of procedures and treated patients one year before and in the first year of the COVID-19 pandemic were compared. (3) Results: In the first year of the COVID-19 pandemic, the number of radiotherapy procedures and cancer patients treated with radiotherapy in Warmia and Masuria voivodeship in Poland was stable compared to the period before the pandemic. The COVID-19 pandemic has not affected the ratio of palliative to radical procedures. The percentage of ambulatory and hostel procedures significantly increased with the reduction of inpatient care in the first year of the COVID-19 pandemic. (4) Conclusion: No significant decrease in patients treated with radiotherapy during the first year of the pandemic in Warmia and Masuria voivodeship in Poland could indicate the rapid adaptation of radiotherapy centers to the pandemic situation. Future studies should be carried out to monitor the situation because the adverse effects of the pandemic may be delayed.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".