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Record W1485335660 · doi:10.1007/bf03403711

Outcomes from a Canadian public health prenatal screening program for hepatitis B: 1997-2004.

2007· article· en· W1485335660 on OpenAlexaffabout
Sabrina S. Plitt, Ali M. Somily, Ameeta E. Singh

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldMedicine
TopicHepatitis B Virus Studies
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsMedicineHBsAgSerologyHepatitis B virusTransmission (telecommunications)Hepatitis BHepatitis B immune globulinPregnancyImmunizationPediatricsObstetricsHepatitis B vaccineAntibodyImmunologyVirus

Abstract

fetched live from OpenAlex

BACKGROUND: Without appropriate prophylaxis, the rate of vertical transmission of hepatitis B virus (HBV) can be as high as 95%. Alberta's provincial prenatal program screens all pregnant women for HBV, and provides prophylaxis to infants born to HBV-infected women. Canadian data on the outcomes of such programs are limited. METHODS: We conducted a retrospective review of data from pregnant Albertan women who were Hepatitis B Surface Antigen (HBsAg) positive from 1997-2004. We describe the frequency of hepatitis B immunoglobulin (HBIG) and vaccine administration, follow-up serology and pregnancy outcomes. RESULTS: In total, 1,485 HBsAg-positive pregnant women were identified; an average of 186 women annually (range: 125-216). Of the 980 infants eligible to have completed prophylaxis and serological follow-up, 82.0% were appropriately immunized and serologically tested, 11.3% had complete immunization but no serology testing and 6.6% were incompletely immunized. Of infants with complete immunization and follow-up, 3.7% failed to mount an immune response and 2.1% were infected. CONCLUSION: A high proportion of infants born to carrier mothers are receiving appropriate post-natal prophylaxis in Alberta. Future research should examine maternal factors that may increase the vertical transmission of HBV.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.494
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.069
GPT teacher head0.305
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2007
Admission routes2
Has abstractyes

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