Assessment of 2 Cervical Screening Methods in Mongolia
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to evaluate the test parameters of visual inspection with acetic acid (VIA) and cervical cytology in 3 Mongolian aimags. METHODS: From February 18, 2002, to December 12, 2004, sexually active women, 30 years or older who had never been screened, underwent cervical cytology and VIA in the aimags' central hospital. Women with abnormal test results and 5% of women with normal results were recommended to have colposcopy with or without biopsy. RESULTS: Two thousand nine women underwent both tests. Visual inspection with acetic acid was abnormal in 254 (12.6%); Pap smear showed atypical squamous cells of undetermined significance or worse in 3%. Using cervical intraepithelial neoplasia 2 or higher disease on biopsy as the end point, the test parameters for VIA are sensitivity of 82.9% (95% CI = 81.3%-84.5%), specificity of 88.6% (95% CI = 87.2%-90.0%), positive predictive value of 12.2% (95% CI = 10.8%-13.6%), and negative predicative value of 99.7% (95% CI = 99.5%-99.9%). The test parameters for Pap smear are sensitivity of 88.6% (95% CI = 87.2%-90.0%), specificity of 98.5% (95% CI = 98.0%-99.0%), positive predictive value of 51.7% (95% CI = 49.5%-53.9%), and negative predicative value of 99.8% (95% CI = 99.6%-100%). CONCLUSION: Visual inspection with acetic acid has an acceptable test parameter for population-based cervical screening in Mongolia.
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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 | 0.003 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".