Determinants of Cervical Cancer Screening Accuracy for Visual Inspection with Acetic Acid (VIA) and Lugol’s Iodine (VILI) Performed by Nurse and Physician
Bibliographic record
Abstract
BACKGROUND: Visual inspection with acetic acid (VIA) and Lugol's iodine (VILI) are used to screen women for cervical cancer in low-resource settings. Little is known about correlates of their diagnostic accuracy by healthcare provider. We examined determinants of VIA and VILI screening accuracy by examiner in a cross-sectional screening study of 1528 women aged 30 years or older in a suburb of Kinshasa, Democratic Republic of Congo. METHODS: We used a logistic regression model for sensitivity and specificity to estimate the diagnostic accuracy of VIA and VILI, independently performed by nurse and physician, as a function of sociodemographic and reproductive health characteristics. RESULTS: Nurses rated tests as positive more often than physicians (36.3% vs 30.2% for VIA, 26.2% vs 25.2% for VILI). Women's age was the most important determinant of performance. It was inversely associated with sensitivity (nurse's VIA: p<0.001, nurse's VILI: p = 0.018, physician's VIA: p = 0.005, physician's VILI: p = 0.006) but positively associated with specificity (all four combinations: p<0.001). Increasing parity adversely affected sensitivity and specificity, but the effects on sensitivity were significant for nurses only. The screening performance of physician's assessment was significantly better than the nurse's (difference in sensitivity: VIA = 13%, VILI = 16%; difference in specificity: VIA = 6%, VILI = 1%). CONCLUSIONS: Age and parity influence the performance of visual tests for cervical cancer screening. Proper training of local healthcare providers in the conduct of these tests should take into account these factors for improved performance of VIA and VILI in detecting cervical precancerous lesions among women in limited-resource settings.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".