The Short-Term Impact Of COVID-19 Pandemic on Cervical Cancer Screening: A Systematic Review and Meta-Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: 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. METHODS: 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. RESULTS: 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%). CONCLUSION: 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 it