Clinical course of 161 untreated and tenofovir‐treated chronic hepatitis<scp>B</scp>pregnant patients in a low hepatitis<scp>B</scp>virus endemic region
Bibliographic record
Abstract
Hepatitis B immunoprophylaxis failure is linked to high maternal viraemia. There is limited North American data on hepatitis B outcomes in pregnancy. Pregnant hepatitis B carriers were enrolled January 2011-December 2014 and offered tenofovir in the 3rd trimester if hepatitis B virus (HBV)-DNA was >7-log IU/mL. Outcomes were determined in treated vs untreated patients. In total, 161 women with 169 pregnancies (one twin, 170 infants; median age 32 years), 18% (29/161) HBeAg+ and median HBV-DNA 2.51 log IU/mL (IQR 1.66-3.65; range 0.8-8.1) were studied. 14.3% (23/161) received tenofovir due to high viral load (16/23, median 74 days, IQR 59-110) or due to liver disease (7/23). In 10/16 treated due to high viraemia, with confirmed adherence, follow-up HBV-DNA showed a 5.49 log decline (P = 0.003). In treatment naïve mothers, median alanine aminotransferase (ALT) increased from 17 IU/L (IQR 12-24) to 29 (IQR 18-36) post-partum (P = 1.5e-7). In seven highly viraemic mothers who declined therapy (HBV-DNA >8-log IU/mL); median ALT increased ~3X from baseline (P < 0.01). 26% (44/169) had Caesarean section with no difference in treated vs untreated subjects. No tenofovir-treated mothers had renal dysfunction. Data were available on 167/170 infants; in 50.8% (85/167) who completed immunoprophylaxis, 98.8% (84/85, including 12 exposed to tenofovir in utero) were HBV immune. One infant born to an HBeAg+ mother with HBV-DNA >8-log IU/mL failed immunoprophylaxis. In this prospective Canadian cohort study, most untreated mothers experienced mild HBV flares. Tenofovir in pregnancy is well tolerated and reduces viral load prior to parturition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".