Assessing the gain in diagnostic performance when two visual inspection methods are combined for cervical cancer prevention
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study was to establish whether combined screening with visual inspection with acetic acid (VIA) and Lugol's iodine (VILI) improves detection of cervical intraepithelial neoplasia 2-3 (CIN 2-3) lesions and cancer beyond chance, compared with screening with VIA alone or VILI alone; and to estimate the extra number of false-positive (FP) results per additional disease case found with the combined test, and to estimate the additional costs involved. SETTING: Ten cross-sectional studies in Burkina Faso, Congo, Guinea, India, Mali and Niger, between 1999 and 2003. METHODS: Using a common protocol, health workers screened 56,147 women aged 25-65 years with VIA and VILI. All women underwent a colposcopy examination and biopsies were taken when necessary. The disease reference standard was histology or negative colposcopy. A positive result on the combined test was defined if either VIA or VILI were positive. The accuracy of the combined test compared with VIA alone or VILI alone was evaluated using likelihood ratios. RESULTS: The estimated sensitivity and specificity were 81.3% and 87.3%, respectively, for VIA; 91.5% and 86.9% for VILI; and 92.9% and 83.5% for the combined test. The ratio of the positive likelihood ratios of the combined test and VIA alone for CIN 2-3 lesions and cancer was 0.88 (95% confidense interval [CI]: 0.86-0.90), favouring use of VIA alone. The ratio of the negative likelihood ratios was 0.40 (95% CI: 0.37-0.47), favouring use of the combined test. Similar results were obtained when the combined test was compared with VILI alone. Assuming equivalent performance of VIA alone and the combined test with a disease prevalence of 2%, there will be about 16.0 (95% CI: 13.6-18.8) additional FPs for each additional true positive (TP) detected if the combined test is used. This number will be 121.1 (95% CI: 75.4-194.6) if VILI is considered as the single test. CONCLUSIONS: At the trade-off point between the combined test and VIA alone or VILI alone, given the numbers of additional FP results involved for each additional TP case of disease that were found, it would be more likely that settings already using VIA would advocate combined testing, and for settings using VILI to opt for the single test. The additional costs (per 1000 women) incurred with the combined test would be International 4117.68 dollars versus either of the tests above.
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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 | 0.027 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".