Results of a community‐based cervical cancer screening pilot project using human papillomavirus self‐sampling in Kampala, Uganda
Bibliographic record
Abstract
OBJECTIVE: To examine the feasibility of a community-based screening program using human papillomavirus (HPV) self-sampling in a low-income country with a high burden of cervical cancer. METHODS: A pilot study was conducted among 205 women aged 30-69years in the Kisenyi district of Kampala, Uganda, from September 5 to October 30, 2011. Women were invited to provide a self-collected specimen for high-risk oncogenic HPV testing by outreach workers at their homes and places of gathering in their community. Specimens were tested for HPV, Neisseria gonorrhoeae and Chlamydia trachomatis. Women who tested positive for HPV were referred for colposcopy, biopsy, and treatment at a regional hospital. RESULTS: Of the 199 women who provided a specimen, 35 (17.6%) tested positive for HPV. The outreach workers were able to provide results to 30 women (85.7%). In all, 26 (74.3%) of the women infected with HPV attended their colposcopy appointments and 4 (11.4%) women were diagnosed with grade 3 cervical intraepithelial neoplasia. CONCLUSION: Self-collection of samples for community-based HPV testing was an acceptable option; most women who tested positive attended for definitive treatment. Self-sampling could potentially allow for effective recruitment to screening programs in limited-resource settings.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".