Massive single visit cervical pre-cancer and cancer screening in eastern Democratic Republic of Congo
Bibliographic record
Abstract
BACKGROUND: In the Democratic Republic of Congo (DRC), practical and affordable strategies for cervical cancer screening are needed to detect and treat pre-cancerous and cancerous lesions in a timely fashion. This study presents the results of mass cervical cancer screenings in eastern DRC using a "screen and treat" approach. METHODS: In two mass cervical cancer screening campaigns, patients underwent a combination of visual inspection of the cervix with acetic acid, visual inspection of the cervix with Lugol iodine solution, and colposcopy with or without loop electrosurgical excision procedure. Cervical biopsy samples were taken for histology analysis. Marital status, age, history of abnormal bleeding, and number of pregnancies were recorded for each patient and association analyses were performed. RESULTS: Of the 644 women who received cervical pre-cancer and cancer screening, 48 had suspicious pre-cancer and cancer lesions that were biopsied (7.45%). On histology analysis cervical intraepithelial neoplasia (CIN) was identified in 15 (2.33%), squamous cell carcinoma (SCC) was identified in 6 (0.93%) and non-neoplastic cervicitis was identified in 11 (1.71%). Abnormal bleeding was significantly associated with CIN/SCC but no significant association was observed for prior pregnancy, patients' home region, or age. CONCLUSION: Forty-eight women with suspicious pre-cancerous or cancerous lesions were successfully identified using the "screen and treat" approach in eastern DRC, suggesting that this approach is feasible for reducing cervical cancer morbidity and mortality. However, community awareness would be necessary, providers would have to be properly trained, referral and follow up mechanisms would have to be put in place, and equipment / supplies would have to be secured if the "screen and treat" approach is to be successful on a wider scale. There is ongoing need for HPV vaccination in DRC as a primary prevention strategy against cervical cancer.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".