An electronic prompt to improve hepatitis B virus screening prior to cancer treatment.
Bibliographic record
Abstract
169 Background: Hepatitis B virus (HBV) reactivation is a potentially fatal complication of cancer therapy that is almost entirely preventable. Despite this, HBV screening rates remain low at many centers. We evaluated the effectiveness of an electronic prompt on HBV screening rates and compared this strategy with education alone. Methods: An education session on HBV reactivation was delivered to all oncology staff at two large, academic oncology centers in the fall of 2010. At one center (study center) an electronic prompt was also introduced. The electronic prompt reminded physicians to screen for HBV when booking a new patient’s first chemotherapy and automatically trigged an electronic order for HBsAg if the physician assented. The prompt was not implemented at the second (control) center. The primary endpoint was the rate of HBV screening. Actual HBV screening rates were determined in both centers for 10 months prior to and for 12 months following the interventions. HBV screening rates were assessed and compared with process control charts (p-charts); 3-sigma limits were employed to define special cause variation. Results: 6,116 new patients received their first chemotherapy during the study period (2,095 study center; 4,021 control center). In the pre-prompt period, the screening rate was stable at 16% at the study center and 25% in the control center. In the prompt period, the screening rate increased to 62% at the study center and was unchanged at 25% in the control center. Special cause variation suggesting a non-random improvement in HBV screening rate was detected at the Study Center two months after the introduction of the electronic prompt. Conclusions: An electronic prompt increased the rate of HBV screening, however screening rates remained relatively low. Education sessions did not appear to improve the HBV screening rate.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".