Tuberculosis and risk of cancer: A systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Cancer is a major cause of death among people who experience tuberculosis (TB), but little is known about its timing and incidence following TB treatment. Our primary objectives were to estimate the pooled risk of all and site-specific malignancies in people with TB compared to the general population or suitable controls. Our secondary objective was to describe the pooled risk of cancer at different time points following TB diagnosis. METHODS: This study was prospectively registered (PROSPERO: CRD42021277819). We systematically searched MEDLINE, Embase, and the Cochrane Database for studies published between 1980 and 2021. We included original observational research articles that estimated cancer risk among people with TB compared to controls. Studies were excluded if they had a study population of fewer than 50 individuals; used cross-sectional, case series, or case report designs; and had a follow-up period of less than 12 months. Random-effects meta-analysis was used to obtain the pooled risk of cancer in the TB population. RESULTS: Of the 5,160 unique studies identified, data from 17 studies were included. When compared to controls, the pooled standardized incidence ratios (SIR) of all cancer (SIR 1.62, 95% CI 1.35-1.93, I2 = 97%) and lung cancer (SIR 3.20, 95% CI 2.21-4.63, I2 = 90%) was increased in the TB population. The pooled risk of all cancers and lung cancer was highest within the first year following TB diagnosis (SIR 4.70, 95% CI 1.80-12.27, I2 = 99%) but remained over five years of follow-up. CONCLUSIONS: People with TB have an increased risk of both pulmonary and non-pulmonary cancers. Further research on cancer following TB diagnosis is needed to develop effective screening and early detection strategies. Clinicians should have a high index of suspicion for cancer in people with TB, particularly in the first year following TB diagnosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.021 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".