Implementation of a Hepatitis B Screening Program in Patients Receiving Systemic Anti-Cancer Therapy
Bibliographic record
Abstract
Cancer patients receiving non-endocrine therapies are at risk of hepatitis B virus (HBV) reactivation (HBVr). Guidelines recommend HBV screening prior to treatment. The Ottawa Hospital Cancer Center implemented a screening pilot for all patients receiving FOLFOX-based regimens between January and April 2023. We assessed the pilot from a quality improvement perspective. Charts were retrospectively reviewed, and patient and disease characteristics were collected. The primary endpoint was to identify the proportion of patients who underwent HBV screening prior to treatment start. Univariate analyses assessed the association between baseline characteristics and failure to screen. Quality metrics were also reviewed. There were 32/42 patients (76.2%) who completed screening, and 5 (11.9%) had a positive screen. The majority of eligible patients (59.5%) completed screening prior to the first treatment as intended. Four of five patients who tested positive were referred to Infectious Diseases. Of those, one received antivirals for chronic HBV. There were no treatment delays due to pending screening and no HBV reactivation. Receipt of prior systemic therapy was significantly associated with failure to screen (55 vs. 95%, OR 17.1 (95% CI 1.92–153), p = 0.011). The results of this pilot highlight the importance of building HBV screening into standardized treatment plans and engaging all team members to ensure high levels of screening. Prior systemic therapy receipt was associated with failure to screen, and thus, programs should include education on the necessity of screening as recommended by medical guidelines.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".