A meta-analysis of mammography screening promotion.
Bibliographic record
Abstract
The purpose of this study was to identify factors that influence the effectiveness of interventions in increasing women's use of mammography screening programs. To this end, we conducted a systematic literature review of studies published between 1966 and 1997. In this review, we recorded data about the year and country in which studies were completed, the study design, the methods for measuring screening rates, various sample characteristics, the nature of the intervention, and the resulting screening rates. The PRECEDE model was used as a framework to make distinctions between the various interventions. To synthesize evidence about the baseline screening rates and the effect of interventions on the incidence of mammography screening, we fit random-effects logistic regression models. These models revealed that more recent studies (those conducted from 1990 to 1996) were associated with higher screening rates (odds ratio [OR], 2.1; 95% confidence interval [CI], 1.2-3.9). Conversely, those designed to target older women (minimum age, 50-65 years) and those set in clinics exhibited smaller screening rates (OR, 0.6, 95% CI, 0.3-1.0, and OR, 0.5; 95% CI, 0.3-0.8, respectively). The meta-analyses also suggested methodologic issues that must be considered before the relative strength of various interventions can be assessed rigorously.
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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 | 0.024 | 0.060 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".