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Record W2338941173 · doi:10.1093/cid/ciw233

Optimal Timing of Immunization Against Pertussis During Pregnancy

2016· letter· en· W2338941173 on OpenAlexaff
Bahaa Abu Raya, Isaac Srugo, Ellen Bamberger

Bibliographic record

VenueClinical Infectious Diseases · 2016
Typeletter
Languageen
FieldImmunology and Microbiology
TopicBacterial Infections and Vaccines
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineImmunizationPregnancyPertussis vaccineWhooping coughBordetella pertussisPediatricsImmunologyObstetricsVaccinationAntibody

Abstract

fetched live from OpenAlex

To the Editor—We read with interest Eberhardt et al's recent study reporting that immunization of pregnant women with tetanus-diphtheria-acellular pertussis (Tdap) during 13–25 gestational weeks (GWs) is associated with higher cord anti–pertussis toxin (PT) and filamentous hemagglutinin (FHA) immunoglobulin G (IgG) geometric mean concentrations (GMCs) and higher expected infant seropositivity rate compared with immunization beyond 26 GWs [1]. These findings are robust and timely given the recognized challenge of protecting young infants from pertussis disease. Moreover, although Eberhardt et al's results support our previous findings that immunization with Tdap during the early third trimester resulted in higher cord anti-PT and FHA IgG GMCs as compared to immunization during the late third trimester [2], several issues are worthy of further discussion. First, it remains to be determined if Tdap immunization in early pregnancy is associated with higher vaccine-specific IgG transfer efficiency compared with immunization later in pregnancy. The predominant IgG transplacental transfer occurs during the third trimester of pregnancy via the neonatal Fc receptor [3]. The efficiency of vaccine-specific IgG transplacental transfer corresponds to the cord-maternal ratio (CMR) of vaccine-specific antibody levels at delivery [4], rather than the cord absolute antibody levels. Several factors, including the concentration of total and vaccine-specific IgG in the maternal sera [4, 5], affect the efficiency of transfer across the placenta. Our previous data revealed that immunization during 27–30 GWs is associated with a significantly higher CMR of anti-PT, FHA, and pertactin IgG compared with immunization during 31–36 GWs [2]. Thus, data on maternal pertussis antibody levels at delivery are of great interest to enhance our understanding of the efficiency of pertussis-specific IgG transplacental transfer. Second, it is yet to be determined whether cord pertussis antibody levels measured at delivery are the result of cumulative transplacental transfer of antibodies during gestation or limited to the transfer that occurs mainly in close proximity to delivery. Earlier studies showed that maternally derived IgG levels in the fetal blood increase as gestation advances [3]. Notably, the amount of maternally derived pertussis antibodies in fetal blood is thought to be affected by the established adult's pertussis antibodies’ half-life; specifically, 36 days for anti-PT IgG and 40 days for anti-FHA IgG [6], with comparable data on infants yet to be established [7]. As such, maternally derived pertussis-specific antibodies accrued in fetal blood during early pregnancy following immunization in early gestation do not assure higher cord pertussis antibody levels at term delivery compared with immunization later in pregnancy, and in fact, their contribution to the total pertussis antibodies may be minimal. Finally, although anti-PT IgG >5 EU/mL is assumed as the seropositivity cutoff [1], the minimal protective level of pertussis-specific antibodies at delivery required to confer protection from pertussis disease in infants remains unknown. What is established is that higher pertussis antibody levels are associated with enhanced clinical protection from disease [8–10]. Thus, additional studies exploring the effect of the timing of Tdap immunization during pregnancy on the clinical protection of young infants from pertussis disease should be undertaken. Potential conflicts of interest. All authors: No reported conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential onflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.017
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0170.016
Insufficient payload (model declined to judge)0.0030.002

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 head0.030
GPT teacher head0.305
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreCommentary

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

Citations19
Published2016
Admission routes1
Has abstractno

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