Community-based self-collected human papillomavirus screening in rural Zimbabwe
Bibliographic record
Abstract
BACKGROUND: In low- and middle-income countries (LMIC), women have limited access to and uptake of cervical cancer screening. Delayed diagnosis leads to poorer outcomes and early mortality, and continues to impede cancer control disproportionately in LMIC. Integrating self-collected, community-based screening for High Risk-Human Papilloma Virus (HR-HPV) into existent HIV programs is a potential screening method to identify women at high risk for developing high-risk cervical lesions. METHODS: We implemented community-based cross-sectional study on self-collection HR-HPV screening in conjunction with existing community outreach models for the distribution of antiretroviral therapy (ART) and the World Health Organization Expanded Program on Immunization (EPI) outreach in villages in rural Zimbabwe from January 2017 through May 2017. RESULTS: Overall, there was an 82% response rate: 70% of respondents participated in self-collection and 12% were ineligible for the study (inclusion criteria: age 30-65, not pregnant, with an intact uterus). Women recruited in the first 2-3 months of the study had more opportunities to participate and therefore significantly higher participation: 81% participation (additional 11% ineligible), while those with fewer opportunities also had lower participation: 63% (additional 13% ineligible) (p < 0.001). Some village outreach centers (N = 5/12) had greater than 89% participation. CONCLUSIONS: Integration of HR-HPV screening into existing community outreach models for HIV and immunizations could facilitate population-based screening to scale cancer control and prevention programs in sub-Saharan Africa. Community/village health workers (CHW/VHW) and village outreach programs offer a potential option for cervical cancer screening programs to move towards improving access of sexual and reproductive health resources for women at highest risk.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".