P32: Hepatitis B screening prior to chemotherapy initiation in a tertiary care center in Québec, Canada
Bibliographic record
Abstract
Reactivation of hepatitis B virus (HBV) during or after chemotherapy is a serious complication of cancer treatment. Worldwide, hepatology societies suggest that clinicians should screen patients for HBV before starting immunosuppressive therapy. Previous research has shown that the rate of HBV reactivation in chronic HBV patients receiving chemotherapy is drastically reduced with the use of a nucleoside analogue. However, limited data exist in Canada about prevalence of HBV screening prior to chemotherapy. We aimed to determine the rate of HBV screening before chemotherapy in our institution. We retrospectively reviewed 784 case files from adult patients starting a first round of intravenous chemotherapy. In order to meet our statistical power estimates, all 2011 cases and a series of the most recent 2010 cases were reviewed. We collected information about chemotherapy type, the medical condition that required treatment, HBV serology and reactivation rate. Screening was considered as satisfactory when any combination of HBV serologic markers had been measured within 12 months of first day of chemotherapy. Of the 784 charts reviewed, 120 (15.3%; CI 95%: 12.8–17.8%) patients were considered to have been satisfactorily screened for HBV. A screening strategy using HBsAg and anti-HBc testing was performed in 103 (13.1%; CI 95%: 10.7–15.5%) patients. Ninety-three (11.9%) patients received rituximab-based chemotherapy. Of this group, 38 (40.9%; CI 95%: 30.9–50.9%) patients underwent satisfactory pre-emptive HBV screening. Statistical analysis showed a significantly higher (p < 0.0001) screening rate in patients receiving rituximab-based therapy compared to other chemotherapy treatments. Only 3 (0.4%) patients had positive HBsAg serology. Of these patients, none received a nucleoside analogue for prophylaxis and one patient, who received rituximab-based therapy, experienced HBV reactivation. In our institution, HBV serology was available in <20% of patients starting chemotherapy and clinicians are more likely to order HBV serology for patients starting rituximab-based chemotherapy. Although our sample size is relatively limited, our data clearly suggest that clinicians are potentially ill-informed about the risks and consequences of HBV reactivation during chemotherapy. Therefore, this study reinforces the need to inform physicians about the importance of pre-emptive HBV screening prior to chemotherapy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".