The impact of the COVID‐19 pandemic on the Ontario Cervical Screening Program, colposcopy and treatment services in Ontario, Canada: a population‐based study
Bibliographic record
Abstract
OBJECTIVE: To describe the immediate impact of the COVID-19 pandemic on cervical screening, colposcopy and treatment volumes in Ontario, Canada. DESIGN: Population-based retrospective observational study. SETTING: Ontario, Canada. POPULATION: People with a cervix age of 21-69 years who completed at least one cervical screening cytology test, colposcopy or treatment procedure for cervical dysplasia between January 2019 and August 2020. METHODS: Administrative databases were used to compare cervical screening cytology, colposcopy and treatment procedure volumes before (historical comparator) and during the first 6 months of the COVID-19 pandemic (March-August 2020). MAIN OUTCOME MEASURES: Changes in cervical screening cytology, colposcopy and treatment volumes; individuals with high-grade cytology awaiting colposcopy. RESULTS: During the first 6 months of the COVID-19 pandemic, the monthly average number of cervical screening cytology tests, colposcopies and treatments decreased by 63.8% (range: -92.3 to -41.0%), 39.7% (range: -75.1 to -14.3%) and 31.1% (range: -43.5 to -23.6%), respectively, when compared with the corresponding months in 2019. Between March and August 2020, on average 292 (-51.0%) fewer high-grade cytological abnormalities were detected through screening each month. As of August 2020, 1159 (29.2%) individuals with high-grade screening cytology were awaiting follow-up colposcopy. CONCLUSIONS: The COVID-19 pandemic has had a substantial impact on key cervical screening and follow-up services in Ontario. As the pandemic continues, ongoing monitoring of service utilisation to inform system response and recovery is required. Future efforts to understand the impact of COVID-19-related disruptions on cervical cancer outcomes will be needed. TWEETABLE ABSTRACT: COVID-19 has had a substantial impact on cervical screening and follow-up services in Ontario, Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".