Risk of TB disease in individuals with cancer
Bibliographic record
Abstract
BACKGROUND: Cancer increases the risk of developing TB disease; however, there are limited data on the magnitude of risk by cancer type and timing after diagnosis of cancer in low TB incidence settings. METHODS: We conducted a nested case-control study of persons in Quebec between 1993 and 2017, including people with TB disease and matched controls. Conditional logistic regression was used to estimate adjusted odds ratios (aORs) of developing TB among people with cancer overall, by sub-type, and by time from cancer to TB diagnosis. RESULTS: There were 4,283 people with TB disease and 268,420 matched controls. The median age for people with TB disease and controls was respectively 46 years (IQR 30-67) and 36 years (24-47). Prior exposure to cancer was associated with TB disease (aOR 6.3, 95% CI 5.3-7.6). The risk of TB diagnosis was highest within 3 months of cancer diagnosis (aOR 26.6, 95% CI 19.6-36.2), with 60% of diagnoses of TB disease occurring within 6 months of cancer diagnosis. CONCLUSION: Risk of TB varies over time and by cancer type. Screening and treatment should be considered for potentially preventable TB (diagnosed more than 6 months post-cancer), particularly in those with respiratory, haematologic, and head and neck cancers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 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".