Effectiveness of the National Population-Based Cervical Cancer Screening Programme in Poland--outcomes, problems and possible solutions 7 years after implementation.
Bibliographic record
Abstract
Cervical cancer is a substantial issue for public health in Poland. In 2006, in order to improve epidemiological data, the National Population-Based Cervical Cancer Screening Programme was developed and implemented. The Programme concerned 9.7 million women aged 25-59 to be screened during a 3-year interval. In 2010, a decline in cervical cancer incidence by 5.7% and 3.4% in mortality rate was observed. However, 5-year survival rates do not exceed 51%. Attendance rate reached 27%, then fell and presently remains on the level of 24%. Currently, the main concern for the screening organizers is searching for areas malfunctioning in local conditions, to improve them, and to provide further progress in cervical cancer prevention. The objective of the presented study was to critically review available data concerning the outcomes of the Screening Program and to suggest possible solutions. Two main factors were taken into account in the study: cost-effectiveness and attendance rate. To encourage attendance, women in Poland are sent personal invitations. This procedure consumes from a quarter up to a half of the budget of the Programme each year, but its effectiveness seems unsatisfactory. In addition to mailing, intensive training of doctors and midwives is conducted. Other activities to increase coverage include developing a social educational campaign. According to the Polish experience, the most effective way to increase coverage is training screening providers and involving them actively in encouraging screening participation. Thus, redistribution of funds from mailing to education and to a social campaign should be considered. Further development of cervical cancer prevention may depend on organizational changes including enhancing reporting, monitoring and quality control in opportunistic screening.
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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".