Implementation of a Routine Screening Program for Latent Tuberculosis Infection among Patients with Acute Leukemia at a Canadian Cancer Center
Bibliographic record
Abstract
Background: Screening for latent tuberculosis infection (LTBI) in patients with hematological malignancy is recommended because of their increased risk of tuberculosis (TB). We assessed the utility of tuberculin skin test (TST) screening in patients with acute leukemia and subsequent outcomes of LTBI treatment. Methods: We retrospectively evaluated patients ≥16 years of age with acute leukemia from 2013–2014 with a TST planted and read prior to the initiation of antineoplastic chemotherapy treatment. Demographics, clinical information and treatment outcomes of LTBI therapy were compared between patients with positive TST (≥10 mm induration) and negative TST. Results: A total of 389 patients with acute leukemia were included in the cohort. Of them, 37/389 (9.5%) had a positive TST. Only 3.4% (8/235) of individuals originating from North and South America as well as the Caribbean were TST positive, while 21% (20/95) of individuals from Asia were TST positive. Diagnostic imaging findings consistent with prior tuberculosis infection were higher in TST positive patients compared to TST negative ones (29.7% versus 9.4%, p < 0.0001). Furthermore, 31/38 patients (81.6%) who were TST positive received LTBI therapy, which was well tolerated. There was no significant difference in overall survival among those who received LTBI therapy compared to those who did not. No patients developed active TB. Conclusions: Universal screening with TST may be of low yield in individuals with acute leukemia unless patients originate from a TB endemic country. When therapy for LTBI is prescribed, patients with acute leukemia do not experience drug-induced liver toxicity and are likely to complete the intended duration of therapy, thus preventing the development of active tuberculosis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".