Mobile Screening Units for the Early Detection of Cancer: A Systematic Review
Bibliographic record
Abstract
Abstract Mobile screening units (MSUs) provide cancer screening services outside of fixed clinical sites, thereby increasing access to early detection services. We conducted a systematic review of the performance of MSUs for the early detection of cancer. Databases (MEDLINE, EMBASE, Cochrane Library, WHO Global Health Library, Web of Science, PsycINFO) were searched up to July 2015. Studies describing screening for breast, cervical, and colon cancer using MSUs were included. Data were collected for operational aspects including the performance of exams, screening tests used, and outcomes of case detection. Of 268 identified studies, 78 were included. Studies investigated screening for cancers including breast (n = 55), cervical (n = 12), colon (n = 1), and multiphasic screening for multiple cancers (n = 10). The median number of screening exams performed per intervention was 1,767 (interquartile range 5,656–38,233). Programs operated in 20 countries, mostly in North America (36%) and Europe (36%); 52% served mixed rural/urban regions, while 35% and 13% served rural or urban regions, respectfully. We conclude that MSUs have served to expand access to screening in diverse contexts. However, further research on the implementation of MSUs in low-resource settings and health economic research on cost-effectiveness of MSUs compared with fixed clinics to inform policymakers is needed. Cancer Epidemiol Biomarkers Prev; 26(12); 1679–94. ©2017 AACR.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".