Implementation of second round of HPV-based screening for cervical cancer in programmatic contexts in Argentina
Bibliographic record
Abstract
ABSTRACT Objectives. To evaluate implementation of the second round of human papillomavirus (HPV)-based cervical screening, introduced in Argentina in 2012–2014 through the Jujuy Demonstration Project for women 30 years and older, and describe the characteristics of women who adhere to the recommended five-year rescreening interval. Methods. A retrospective cohort study was conducted based on the data of two rounds of screening. All women aged 30 years or older who had been HPV-tested during the Jujuy Demonstration Project and had a negative result were included. The Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework was used to evaluate implementation. Multivariable regression was used to examine factors associated with adherence to rescreening. Results. Of 42 307 HPV-negative women, 47.9% (n = 20 285) were rescreened in the second round (Reach); 69.2% of health centers provided at least one HPV test at second-round screening (Adoption); and 13.3% (n = 5 639) of women were rescreened within the recommended five-year interval. Among the total rescreened, 9.3% (n = 1 888) were HPV-positive, of which 95.0% underwent triage Pap and 79.2% of the HPV-positive/abnormal Pap women had colposcopy. Considering women rescreened at five years, the second-round detection rate was 5.3/1 000 screened women. Rescreening at five years was significantly higher among women aged 30–44, with public health insurance, and those living in the provincial capital. Conclusions. Rescreening of HPV-negative women faced challenges linked to its reduced reach, especially if we consider the recommended five-year interval. Our findings suggest that we need to devise specific strategies to increase second-round screening rates.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".