Increasing screening for latent tuberculosis in patients with hematologic malignancy: A quality improvement project.
Bibliographic record
Abstract
264 Background: Latent tuberculosis infection (LBTI) affects about one quarter of the world population. Treating LTBI is the most effective way of preventing active TB. Patients with hematologic malignancy (HM) and LTBI are at heightened risk (2-40 times baseline) of progressing to active TB. Universal TB screening is recommended for this population. In 2016, a patient receiving chemotherapy for myeloma at our center developed active TB. This triggered an intensive contact tracing investigation involving local public health units, infection control, and specialists. Over 800 patients had one or more exposures to the index case. No cases of secondary TB were identified; however, the contact tracing was resource intensive and stressful for patients and staff. This project aims to prevent future incidents by developing a standardized process for completing and documenting TBSTs, ensuring LTBIs are identified and managed prior to systemic therapy for HM. We aimed to complete and document TBSTs in ≥85% of patients starting systemic therapy for HM by Feb 2018. Methods: Baseline data was determined via retrospective review of 25 patients receiving chemotherapy for HM Jul-Nov 2017. An order set, work flow process map and standard operating procedure were created over a series of plan-do-study act cycles. TBST was added to a pre-existing “ready for treatment” checklist employed by pharmacists at our site. When pharmacists could find no record of a TBST result prior to treatment, the treating physician was notified. The go-live date was Aug 15, 2018. The impact of change was evaluated via chart review of patients who started chemotherapy for HM between Aug 27, 2018-Feb 27, 2019. Results: Prior to implementation of the standardized process, 3/25 patients (12%) had documentation of prior TBST. Following implementation of the standardized process, 14/30 patients (47%) had documentation of TBST prior to starting systemic therapy for HM. All 14 patients had negative TBSTs. Conclusions: Developing a standardized process for ordering, completing, and documenting TBST for patients starting systemic therapy for HM is feasible and effective. Additional revisions of the process are required to meet our target.
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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 | 0.028 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".