Impacts of the COVID-19 pandemic on diagnosis of renal cell carcinoma and disease stage at presentation
Bibliographic record
Abstract
INTRODUCTION: Renal cell carcinoma (RCC) is often associated with significant morbidity and mortality, with overall survival contingent on multiple factors - most importantly, disease stage at diagnosis. Disruptions in healthcare delivery during the COVID-19 pandemic have resulted in various reported diagnostic and treatment delays, which have had detrimental impacts on malignancies such as RCC. METHODS: Surgically managed cases of RCC at our center were identified using a retrospective chart review of all nephrectomies conducted from March 1, 2018, to February 28, 2023. Examination of disease characteristics in three time period cohorts (before, during, and following the COVID-19 pandemic) was undertaken. Timeframes were consistent with implementation and abolition of public health restrictions in the province of Newfoundland and Labrador. RESULTS: A total of 483 surgically managed RCC cases were identified during the study period. The median age was 65 years (interquartile range [IQR] 56-71), and 62.3% of patients were male. Demographics did not vary across timeframes. Before and during the pandemic, pathologic stage 3 (pT3) disease was reported in 38.9% and 35.4% of cases, respectively, whereas the post-pandemic period saw this presentation in 50.0% of patients. Surgical wait times increased significantly across study timeframes (p=0.003). CONCLUSIONS: The first year following the COVID-19 pandemic saw an 11.1% increase in patients presenting with pT3 RCC. These findings are suggestive of a clinically significant stage migration, which paired with prolonged wait times for surgery, provide critical consideration in the urgency of diagnostic and treatment decisions for RCC in the immediate future.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".