Poor Recognition of Risk Factors for Hepatitis B by Physicians Prescribing Immunosuppressive Therapy: A Call for Universal Rather than Risk-Based Screening
Bibliographic record
Abstract
BACKGROUND: Reactivation of hepatitis B virus (HBV) during immunosuppressive therapy (IST) can lead to severe and even fatal hepatitis but can be largely prevented with prophylactic antiviral therapy. Screening for HBV prior to starting IST is recommended. Both risk-based and universal screening have been recommended by different societies. For effective risk-based screening, physicians must be aware of risk factors for chronic HBV infection. METHODS: The HBV screening practices prior to starting IST of rheumatologists, medical and hematological oncologists were evaluated by survey and chart review. Country of origin, the primary risk factor for HBV exposure, was determined in all patients. RESULTS: Of 140 rheumatology, 79 medical oncology and 53 hematology patients reviewed, 81%, 11% and 81% were deemed to be at high risk of HBV reactivation by their physicians respectively, however only 27%, 6% and 62% (p<0.0001) were actually screened for HBV prior to starting IST. For patients from HBV-endemic regions, more hematology patients (53%) were correctly identified by their physicians as being at high risk of reactivation than rheumatology patients (2.4%, p=0.0001) or medical oncology patients (15%, p=0.009). However actual screening rates were not increased in patients from endemic regions. A total of 81 patients were screened for HBsAg; 2 were positive. Of the 33 patients screened for anti-HBc, 10 (30%) were positive. CONCLUSIONS: Hematologists, rheumatologists and medical oncologists had low rates of screening for HBV prior to prescribing IST, largely due to poor identification of those at risk for infection. Risk-based screening strategies are unlikely to be effective and should be replaced by universal screening.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".