Integrated Cancer Screening Performance Indicators: A Systematic Review
Bibliographic record
Abstract
Cancer screening guidelines recommend that women over 50 years regularly be screened for breast, cervical and colorectal cancers. Population-based screening programs use performance indicators to monitor uptake for each type of cancer screening, but integrated measures of adherence across multiple screenings are rarely reported. Integrated measures of adherence that combine the three cancers cannot be inferred from measures of screening uptake of each cancer alone; nevertheless, they can help discern the proportion of women who, having received one or two types of screening, may be more amenable to receiving one additional screen, compared to those who haven't had any screening and may experience barriers to access screening such as distance, language, and so on. The focus of our search was to identify indicators of participation in the three cancers, therefore our search strategy included synonyms of integrated screening, cervical, breast and colorectal cancer screening. Additionally, we limited our search to studies published between 2000 and 2015, written in English, and pertaining to females over 50 years of age. The following databases were searched: MEDLINE, EMBASE, EBM Reviews, PubMed, PubMed Central, CINAHL, and Nursing Reference Center, as well as grey literature resources. Of the 78 initially retrieved articles, only 7 reported summary measures of screening across the three cancers. Overall, adherence to cervical, breast and colorectal cancer screening ranged from around 8% to 43%. Our review confirms that reports of screening adherence across breast, cervical and colorectal cancers are rare. This is surprising, as integrated cancer screening measures can provide additional insight into the needs of the target population that can help craft strategies to improve adherence to all three screenings.
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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 | 0.017 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.018 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".