Evaluation of HBV-DNA Monitoring after Completion of Chemotherapy using a PDCA Cycle following Introduction of a Support System Provided by a Multidisciplinary Team of Quality Management in Cancer Medicine
Bibliographic record
Abstract
Background: Reactivation of the hepatitis B virus (HBV) during or after chemotherapy remains a notable clinical concern, particularly among patients with previous exposure to HBV. However, in clinical practice, adherence to HBV-DNA monitoring after completing chemotherapy is often sub-optimal. Methods: We developed and implemented a support system based on the plan–do–check–act (PDCA) cycle to ensure 12-month HBV-DNA monitoring after the completion of chemotherapy. This system was designed to enable continuous follow-up after a cessation of chemotherapy, and a multidisciplinary team of quality management in cancer medicine established a feedback system to provide timely information for physicians. Adherence to HBV-DNA monitoring before and after introduction of the system was compared, and the reasons for discontinuation were investigated. Results: Compared with the pre-intervention group, there was a significant improvement in the rate of HBV-DNA monitoring in the post-intervention group (p < 0.01). In this group, 16 patients (33.3%) were lost to follow-up after chemotherapy due to death or transition to hospice or home-based care. Conclusions: The support system provided by a multidisciplinary team of quality management in cancer medicine effectively improved adherence to HBV-DNA monitoring after the completion of chemotherapy. However, it also revealed that some patients could not be followed up immediately after the completion of treatment given their deteriorating general condition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".