Integrated cervical cancer screening in Mayuge District Uganda (ASPIRE Mayuge): a pragmatic sequential cluster randomized trial protocol
Bibliographic record
Abstract
BACKGROUND: Cervical cancer is almost entirely preventable through vaccination and screening, yet remains one of the 'gravest threats to women's lives' according to the World Health Organization. Specific high-risk subtypes of human papillomavirus (HR-HPV) are well-established as the primary cause of cervical cancer. Uganda has one of the highest cervical cancer incidence rates in the world (54.8 per 100,000) as a result of limited screening access and infrastructure. The integration of a self-collected cervical cancer screening program using HPV testing within existing community-based primary health care services could increase access to screening and reduce cervical cancer rates among Ugandan women. METHODS: Using a pragmatic, sequential, cluster randomized trial design; we will compare the effectiveness of two cervical cancer screening models for self-collected HPV testing: 1) community health worker recruitment (door-to-door); and 2) community health meetings. In Mayuge district, Uganda, 31 villages are randomized to one of two treatment arms. Due to the nature of this trial, blinding is not possible. Women are eligible to participate if they have no previous history of hysterectomy or treatment for cervical cancer or pre-cancer and are aged 25-49 years old. All participants receive an integrated package of cervical cancer screening and education. Samples are tested for HPV using GeneXpert point of care testing. All women who test positive for HR-HPV types are referred to a designated health centre for follow-up inspection by Visual Inspection with Acetic acid (VIA) and treatment with thermal ablation. The primary outcome for the trial is the number of women who attend follow-up for VIA screening at a designated Health Centre after a positive HR-HPV test out of all women screened per arm. Secondary outcomes include: cervical cancer screening knowledge; patient-reported experience measures for self-collected cervical cancer screening; and HPV incidence. DISCUSSION: Results from this study will inform the national scale-up of cervical cancer screening in Uganda, aligning with the World Health Organization's target of achieving cervical cancer elimination through the pillar of increased HPV screening coverage. TRIAL REGISTRATION: ISRCTN, ISRCTN12767014. Registered 14 May 2019, https://doi.org/10.1186/ISRCTN12767014; clinicaltrials.gov, NCT04000503; Registered 27 June 2019, https://clinicaltrials.gov/ct2/show/NCT04000503 PROTOCOL VERSION: January 8, 2020, version 1.
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How this classification was reachedexpand
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.009 | 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 teacher head, 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".