Risk of Active Tuberculosis in Patients With Cancer: A Systematic Review and Metaanalysis
Bibliographic record
Abstract
Background: Cancer is a known risk factor for developing active tuberculosis (TB). We determined the incidence and relative risk of active TB in cancer patients compared to the general population. Methods: Electronic databases were searched up to December 2015: Medline, Medline InProcess, EMBASE, PubMed, the Cochrane Database of Systematic Reviews, Cancerlit, and Web of Science. Studies of pathologically confirmed cancer patients were included if active TB was identified concurrently or after the diagnosis. Cumulative incidence rate/100,000 population (CIR) of new cases of TB occurring in cancer patients and comparative incidence rate ratios (IRR) to the general population from the same country of origin were estimated. A random effect meta-analysis was conducted on the CIR and IRR. Results: A total of 23 studies reporting 593 TB cases occurring in 324,041 cancer patients between 1950 and 2011 were identified. In a meta-analysis of 6 studies conducted in the US in 317,243 cancer patients (98% of all patients) the CIR of active TB decreased by 3 fold and 6.5 fold in hematologic and solid cancers respectively before and after 1980. After 1980 the CIR of active TB was highest in hematologic (219/100,000 population, IRR=26), head and neck (143; 16), lung cancers (83; 9) and was lowest in breast and other solid cancers (38; 4). Conclusions: Individuals living in the US with hematologic, head and neck, and lung cancers had a 9-fold higher rate of developing active TB compared to those without cancer and would benefit from targeted latent TB screening and therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".