Triage strategies in cervical cancer detection in Mexico: methods of the FRIDA Study
Bibliographic record
Abstract
OBJECTIVE: This paper describes the study design and baseline characteristics of the study population, including the first 30 829 women who enrolled in the Forwarding Research for Improved Detection and Access for Cervical Cancer Screening and Triage (FRIDA Study). This is a large population based study that is evaluating the performance and cost-effectiveness of different triage strategies for high-risk HPV (hrHPV) positive women in Mexico. MATERIALS AND METHODS: The target population is more than 100 000 women aged 30 to 64 years who attend the Cervical Cancer Screening Program in 100 health centers in the state of Tlaxcala, Mexico. Since August 2013, all women in the region have been invited to enroll in the study. The study participants are evaluated to determine hrHPV infection using the Cobas 4800 HPV test. The HPV-16/18 genotyping and cytology triage strategies are performed as reflex tests in all hrHPV-positive participants. Women with a positive HPV-16/18 test and/or abnormal cytology (atypical squamous cells of undetermined significance or worse, ASCUS+) are referred for colposcopy evaluation, where a minimum of four biopsies and an endocervical sample are systematically collected. Histologic confirmation is performed by a standardized panel of pathologists. RESULTS: Among the 30 829 women who have been screened, the overall prevalence of hrHPV is 11.0%. The overall prevalence of HPV16 and HPV18 are 1.5% and 0.7%, respectively. Cytological abnormalities (ASCUS+) were detected in 11.8% of the hrHPV-positive women. A total of 27.0% (920/3,401) of the hrHPV-positive women were referred to colposcopy because of a positive HPV16/18 test and/or abnormal reflex cytology, (31.6% had only ASCUS+, 53.6% were HPV16/18 positive with a normal cytology result, and 9.5% were positive to both triage tests). CONCLUSION: The results of this study will help policy makers and health service providers establish the best practices for triage in cervical cancer screening in Mexico and other countries.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".