Risk of Colorectal Cancer and Associated Mortality in HIV: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: As people with HIV live longer, the numbers of colorectal cancer cases are expected to increase. We sought to compare the colorectal cancer incidence and cause-specific mortality among people living with and without HIV. DESIGN: Systematic review and meta-analysis. METHODS: We searched 5 electronic databases up to June 28, 2016, for primary studies reporting standardized incidence ratios (SIRs), standardized mortality ratios (SMRs)/hazard ratios or data sufficient for estimating these summary measures. We performed a random effects pooled analysis to estimate SIR and SMR of colorectal cancer in HIV. RESULTS: Of 8110 articles, we included 27 studies from North America (n = 18), Europe (n = 7), the Pacific region (n = 4), and South America (n = 1). Overall, 1660 cases of colorectal cancer and colon cancer (excluding rectal cancer) occurred among 1,696,070 persons with HIV. In pooled analysis, we found no summary risk of malignancy among those with HIV relative to an uninfected population (SIR 1.00; 95% confidence interval 0.82 to 1.22; I = 89.2%). Colorectal cancer-specific mortality was higher among people with HIV but did not reach statistical significance (SMR 2.09; 95% confidence interval: 1.00 to 4.40; I = 85.0%). CONCLUSIONS: Rates of colorectal cancer are similar between people with and without HIV. Existing screening guidelines are likely adequate for people with HIV.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.032 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".