The Short-Term Impact Of COVID-19 Pandemic on Cervical Cancer Screening: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
A systematic review and meta-analysis were carried out to assess the pooled proportion of women screened for cervical cancer before and during the COVID-19 pandemic.After ruling out registered or ongoing systematic reviews in the PROSPERO database regarding the impact of the COVID-19 pandemic in cervical cancer screening, the protocol of our systematic review and meta-analysis was registered in PROSPERO (CRD42021279305). The electronic databases were searched for articles published in English between January 2020 and October 2021and the study was designed based on PRISMA guidelines updated in 2020. Meta-analysis was accomplished in STATA version 13.0 (College Station, Texas 77,845 USA). The pooled proportion of women who had undergone cervical cancer screening was reported with 95% CI. In order to quantify the heterogeneity, Chi2 statistic (Q statistic) and I2 index were used.The meta-analysis included seven studies from Slovenia, Italy, Ontario (Canada), Scotland, Belgium, and the USA, comprising 403,986 women and 199,165 women who were screened for cervical cancer before the COVID-19 pandemic in 2019 and during the pandemic in 2020, respectively. The pooled proportion of women screened for cervical cancer in 2019 was 9.79% (95% CI 6.00%-13.59%, 95% prediction interval 0.42%-23.81%). During the pandemic, the pooled proportion of screened women declined to 4.24% (95% CI 2.77%-5.71%, 95% prediction interval 0.9%-17.49%).There was a substantial drop in the cervical cancer screening rate due to lockdowns and travel restrictions to curb the COVID-19 pandemic. Scaling up cervical cancer screening strategies is essential to prevent the long-term impact of cervical cancer burden.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Meta-analysis estimating the pandemic's impact on cervical cancer screening rates; a synthesis used to answer an epidemiological question.
This systematic review estimates changes in cervical cancer screening, not how evidence syntheses are conducted.
Systematic review/meta-analysis estimating cervical screening rates under COVID-19; synthesis used to answer a health question.
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 | 0.024 | 0.049 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.063 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".