Evaluation Of Bladder Cancer Care In Ontario, Canada During The Covid-19 Pandemic
Bibliographic record
Abstract
Introduction The COVID-19 pandemic may have caused delays in care for patients with bladder cancer (BC). This study aims to investigate its impact on oncologic outcomes of BC in Ontario, Canada. Methods The Institute for Clinical Evaluative Sciences (ICES) databases were used as the data source. The patients were divided into 2 groups: pre-COVID era (January 1, 2016, to March 14, 2020) and the COVID era (March 15, 2020 to December 31, 2021). The study compared BC stage at diagnosis, surgical volumes, wait times, and overall survival (OS) between the groups. Results There were 17,760 patients included. No significant difference was found in the pathological stage and in the number of BC diagnoses between the two era groups. Patients in the COVID era underwent earlier transurethral resection of bladder tumor. Lastly, after a 2-year follow-up, there was no significant difference in OS rates. Conclusions Patients diagnosed with BC in Ontario during the pandemic did not have more advanced stages of cancer at the time of presentation or worse OS rates compared to those diagnosed before the pandemic. Furthermore, surgical volumes and wait times were not compromised during this period.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".