Evaluation of a Routine Screening Program with Tuberculin Skin Testing on Rates of Detection of Latent Tuberculosis Infection and Prevention of Active Tuberculosis in Patients with Multiple Myeloma at a Canadian Cancer Centre
Bibliographic record
Abstract
Background: Chemotherapy-induced T cell dysfunction, resulting from treatment of multiple myeloma (mm), enhances the risk for reactivation of latent tuberculous infection (ltbi). However, routine screening for ltbi has its limitations. The objective of the present study was to assess the number of patients treated for ltbi both before and after the introduction of a consistent tuberculin skin test (tst) screening program for patients with mm at our cancer centre. Methods: This retrospective observational study analyzed adult patients with mm treated with autologous hematopoietic stem-cell transplantation from 1 January 2013 to 31 December 2014, for whom tst was consistently performed at our cancer facility. Baseline demographic characteristics of patients who received tst testing and ltbi therapy were compared with those of a pre-intervention cohort of patients (1 January 2008 to 31 December 2009) who were not tested. Results: During the post-intervention period, 170 patients with mm had a tst. In 14 patients (8.2%) results were positive, and 11 of the 14 received ltbi therapy. Of another 12 patients with radiographic imaging changes consistent with prior granulomatous disease and negative tst results, 2 were treated. No cases of tuberculosis (tb) reactivation were noted in individuals who completed ltbi therapy. One case of active tb was diagnosed in a patient with a negative tst. In contrast, in the pre-intervention matched cohort of 170 patients, no tsts were performed, and no cases of active tb were documented. Conclusions: Patients with mm could benefit from a consistent tst testing policy coupled with subsequent ltbi therapy. However, universal testing might not be required. A targeted program combining evaluation of host risk factors, imaging findings, and screening tests might optimize ltbi diagnosis and management, and thus be effective in preventing the development of active tb in at-risk patients with mm.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".