Self-sampling and HPV DNA testing for cervical precancer screening in a cohort of nuns in Ghana: a cross-sectional cohort study
Bibliographic record
Abstract
Background: The need for cervical cancer screening has been emphasised in at-risk cohorts of women to reduce their risk of cervical cancer. Some women with decreased risk of acquiring human papillomavirus (HPV) infections, such as Catholic nuns, receive less attention and on occasion are missed in cervical cancer screening programmes. This study aimed to determine the high-risk HPV (hr-HPV) prevalence in such a cohort to emphasise the need for cervical precancer screening among all women. To improve compliance, we employed self-sampling. Methods: This descriptive cross-sectional cohort study involved the data of 105 Catholic nuns subjected to cervical screening using self-samples in the Greater Accra, Volta, and Central regions of Ghana between June 4, 2022 and June 30, 2022. hr-HPV testing was performed on self-samples using the MA-6000 HPV DNA platform. Screen-positive nuns underwent follow-up pap smears and EVA colposcopy. In addition to descriptive analysis, univariate and multivariable nominal logistic regression was used to explore the relationship between hr-HPV positivity and selected continuous and categorical factors. Findings: genotype(s) (n = 1, 1.0%)]. Pap smears for all 25 hr-HPV-positives came in as negative for intraepithelial lesions or malignancy, whereas EVA mobile colposcopy showed minor abnormal findings in two (8.0%; 95% CI, 1.0-26.0), both of whom were managed conservatively. Interpretation: Our findings suggest that the hr-HPV prevalence in this cohort of nuns is similar to that of the general population. To meet the World Health Organization's target for cervical cancer elimination, it is important that all women are given access to cervical cancer screening and preventative services. Further, increasing 'anonymity' and privacy among nuns through self-sampling may be crucial to expanding choice, coverage, and uptake of screening in support of their health rights. Funding: None.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".