Screening for hepatitis B virus prior to chemotherapy: A quality improvement project.
Bibliographic record
Abstract
158 Background: Hepatitis B virus (HBV) reactivation is a well-recognized and serious complication of chemotherapy which can be prevented with prophylactic antiviral therapy. St. Michael’s Hospital (SMH) is an academic hospital in Toronto, Canada, serving an inner city population. The prevalence of chronic HBV in populations such as ours is estimated to be > 8%, compared to 2% of all Canadians. In this context, surveillance for HBV prior to chemotherapy is very important. Aim: To increase the HBV screening rate among patients starting IV chemotherapy at SMH to greater than 90% by December 2013. Methods: Repeated plan-do-study-act (PDSA) cycles targeting HBV screening in our chemotherapy unit were initiated in January 2013 and are on-going. Appropriate HBV screening was defined as at least one HBsAg test up to 3 months prior to, or 3 weeks after starting chemotherapy. Interventions included education sessions, posters, standardized HBV lab order sets, and pharmacist review of lab data prior to first chemotherapy with reminders to physicians when HBV testing was absent. Pre and post-intervention HBV screening rates were compared using process control charting. Results: Between January 1, 2012, and June 15, 2013, 407 unique patients started IV chemotherapy at SMH. Prior to our interventions a stable HBV screening rate of approximately 30% was observed. Sequential process improvements were introduced in January and April 2013. Process control charting demonstrated the presence of special cause variation subsequent to our interventions with a significant improvement in the HBV testing rate (post-intervention rate of 70%). The HBV testing rate began to improve in January 2013 and met criteria for special cause variation by February 2013. Conclusions: It is possible to dramatically increase the rate of HBV testing prior to chemotherapy through relatively simple, low tech process improvements. Further improvements are necessary to reach our goal of a 90% HBV screening rate prior to IV chemotherapy. Additional planned interventions include individualized physician report cards on HBV screening rates using achievable benchmark criteria and an education campaign directed at empowering patients to ask about HBV testing.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".