Predictive value of TNF-α, IFN-γ, and IL-10 for tuberculosis among recently exposed contacts in the United States and Canada
Bibliographic record
Abstract
BACKGROUND: We examined cytokine immune response profiles among contacts to tuberculosis patients to identify immunologic and epidemiologic correlates of tuberculosis. METHODS: We prospectively enrolled 1272 contacts of culture-confirmed pulmonary tuberculosis patients at 9 United States and Canadian sites. Epidemiologic characteristics were recorded. Blood was collected and stimulated with Mycobacterium tuberculosis culture filtrate protein, and tumor necrosis factor (TNF-α), interferon gamma (IFN-γ), and interleukin 10 (IL-10) concentrations were determined using immunoassays. RESULTS: Of 1272 contacts, 41 (3.2%) were diagnosed with tuberculosis before or < 30 days after blood collection (co-prevalent tuberculosis) and 19 (1.5%) during subsequent four-year follow-up (incident tuberculosis). Compared with contacts without tuberculosis, those with co-prevalent tuberculosis had higher median baseline TNF-α and IFN-γ concentrations (in pg/mL, TNF-α 129 versus 71, P < .01; IFN-γ 231 versus 27, P < .001), and those who subsequently developed incident tuberculosis had higher median baseline TNF-α concentrations (in pg/mL, 257 vs. 71, P < .05). In multivariate analysis, contact age < 15 years, US/Canadian birth, and IFN or TNF concentrations > the median were associated with co-prevalent tuberculosis (P < .01 for each); female sex (P = .03) and smoking (P < .01) were associated with incident tuberculosis. In algorithms combining young age, positive skin test results, and elevated CFPS TNF-α, IFN-γ, and IL-10 responses, the positive predictive values for co-prevalent and incident tuberculosis were 40 and 25%, respectively. CONCLUSIONS: Cytokine concentrations and epidemiologic factors at the time of contact investigation may predict co-prevalent and incident tuberculosis.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".