Rates of Screening for Breast, Colorectal, and Cervical Cancers in Older People With Cognitive Impairment or Dementia: A Meta-Analysis
Bibliographic record
Abstract
Purpose: Cancer screening may not be appropriate for some older people. We compare the likelihood of screening for colorectal, breast, and cervical cancers in older people with versus without cognitive impairment or dementia. Method: Systematic search of MEDLINE, Embase, and PsycINFO (to March 9, 2018) for articles reporting screening for colon, breast, and cervical cancers in patients with and without cognitive impairment or dementia. Studies were summarized quantitatively (random effects meta-analysis), according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results: Studies reported data 1989-2008. The rate of screening for breast cancer by mammography was lower in women with cognitive impairment or dementia compared with those without (pooled odds ratio [OR] = 0.81, 95% confidence interval [CI] = [0.71, 0.91], p = .0007, six studies, N = 18,562). The rates of screening for cervical cancer by Pap smear (pooled OR = 0.88, 95% CI = [0.71, 1.08], p = 0.22, five studies, N = 409,131) and colorectal cancer by fecal occult blood test (pooled OR = 0.87, 95% CI = [0.55, 1.38], p = .55, two studies, N = 2,718) were not significantly lower in people with cognitive impairment or dementia. Conclusion: These historical rates provide a baseline for discussions around the need for more specific guidance to assist with decisions to discontinue screening. The study also identifies a gap in reported knowledge with respect to screening under current guidelines.
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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 | 0.026 | 0.055 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.078 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".