COVID-19 Pandemic Impact on Delays in Diagnosis and Treatment for Cervical Cancer in Montreal, Canada
Bibliographic record
Abstract
Introduction. The COVID-19 pandemic has been responsible for a major reorganization of healthcare systems, with less access for cancer screening. Few data exist on the impact of cervical cancer treatment during the pandemic. Methods. The purpose of this study was to compare the cervical cancer stage at diagnosis and the surgical and medical treatment delays before and during the COVID-19 pandemic. This is a retrospective cohort study of all cervical cancers diagnosed at any stages between 1 January 2018 and 28 February 2022 at the Centre Hospitalier de l’Université de Montréal. Stage at diagnosis, time to initial referral, time from diagnosis to treatment before and during the COVID-19 pandemic were compared. Results. A total of 244 cervical cancers were diagnosed during the study period. No differences were observed between the number of cases diagnosed before and after pandemic (p = 0.237). Most patients and disease characteristics did not differ between the study periods, but the patients were significantly younger (p = 0.007), with higher BMI (p = 0.024) in the pandemic period. The mean time between initial diagnosis and referral was longer during the pandemic by 13 days (p = 0.042). The mean time between diagnosis and MRI and diagnosis and PET CT was not longer during the pandemic (p = 0.481 and p = 0.384). There were no significant differences in the mean time from the initial referring to the first visit at the CHUM (p = 0.895) or in the mean time from diagnosis to treatment (0.668) and duration of treatment (p = 0.181) Conclusion. Minor delays were observed during the COVID-19 pandemic. Cervical cancer patients treated at the CHUM, a tertiary and quaternary Canadian public health center, were globally referred and treated similarly, as those who were treated before pandemic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".