Visual inspection as a cervical cancer screening method in a primary health care setting in Africa
Bibliographic record
Abstract
We evaluated the feasibility and performance of visual inspection with acetic acid (VIA) and Lugol's iodine (VILI) for cervical cancer screening in a primary health-care setting in Kinshasa, Congo. Women (1,528) aged > or =30 years were screened independently by nurses and physicians by VIA and VILI and Pap cytology. Biopsy samples were obtained from women with abnormal colposcopies and from 290 randomly chosen women with normal colposcopy. Cytological and histological examinations were performed in Lyon and Montreal, respectively. The prevalence of cervical intraepithelial neoplasia (CIN) of grades 1, 2 and 3 was 4.5, 1.3 and 4%, respectively. Using biopsy as the reference, the sensitivity, specificity and negative predictive value (NPV) for > or =CIN 2 for VIA-nurse were 55.5% (95% CI: 34.7-76.2), 64.6% (95% CI: 62.0-67.1) and 96.8% (95% CI: 93.5-98.7), respectively. The corresponding values for VILI-nurse were 44.0% (95% CI: 24.2-63.8), 74.6% (95% CI: 72.3-76.9) and 96.7% (95% CI: 93.7-98.6). The equivalent parameters for physicians were 71.1% (95% CI: 46.7-95.5), 71.3% (95% CI: 68.9-73.6) and 98.6% (95% CI: 96.0-99.7) for VIA and 68.3% (95% CI: 42.5-94.0), 76.2% (95% CI: 74.0-78.4) and 97.2% (95% CI: 95.3-98.5) for VILI. The sensitivity of cytology ranged between 31 and 72%, depending on the abnormality threshold used to define positivity, with a corresponding specificity range of 94-99% and a NPV range of 97-99%. Our results show that VIA and VILI performed by nurses and physicians are slightly more sensitive but less specific than Pap cytology across multiple combinations of test and lesion thresholds. Given their lower cost and easy deployment, visual inspection methods merit further assessment as cervical cancer screening methods for low-resource countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".