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Record W4234408179 · doi:10.1542/peds.109.2.350b

Seroprotection Rates After Late Doses of Hepatitis B Vaccine

2002· article· en· W4234408179 on OpenAlexaff
Bernard Duval, Geneviève Deceuninck

Bibliographic record

VenuePEDIATRICS · 2002
Typearticle
Languageen
FieldMedicine
TopicHepatitis B Virus Studies
Canadian institutionsHôpital du Saint-Sacrement
Fundersnot available
KeywordsTiterMedicineHepatitis B vaccineVaccinationAntibody titerHepatitis BVirologyBooster (rocketry)ImmunologyAntibodyHepatitis B virusVirus

Abstract

fetched live from OpenAlex

We read with interest the recent article by Middleman et al.1 It shows what we can expect from a clinic-based hepatitis B program targeting adolescents. However, we were quite deluded of the analysis of data. Indeed, several questions emerged after the reading: In Reply—We appreciate the interest and attention with which our article was read by Drs Duval and Deceuninck. They have made some interesting points to which we will respond.Drs Duval and Deceuninck are quite right: antibody titer levels do decay over time. The decline in titer levels, however, is not a simple exponential function of time.2 In fact, titer levels may even rise for some time after a “booster,” and then the titer level decline is initially steep and subsequently flattens. The drop appears steepest during the first 3 months after vaccination.2 Because this first 3 months is the time of greatest titer level flux, we chose to look only at titer levels that were obtained 3 months or more after the last injection of vaccine. This assumes that a more stable titer level has been established that is not changing rapidly after the steeper decline in titer has occurred. The continuous variable of time between the last dose of vaccine and titer level acquisition (the time of titer level acquisition was preset at approximately 12 months after the first dose of vaccine) was not included in the analysis because this time period was dependent on the time between the other injections. Given the extreme level of complexity that this would add to the analysis, the decision was made to assume relative stability of the titer levels 3 months after the latest dose of vaccine.We also agree with the doctors from Québec that the number of unprotected individuals (those who have not achieved seroprotection from the hepatitis B vaccination series) may be overestimated by the figures reported in the paper. Because titer levels may have fallen between vaccination and titer level determination, some individuals in the study who had titer levels below 10 IU/L at 12 months may have achieved seroprotective levels of antibody initially that then declined to below 10 IU/L by the time blood was drawn for the titer level. However, given the way in which adolescents returned for this study, it was not possible to have participants return 2 months after each immunization to establish a standard measurement of titer levels. As one can determine from the subjects’ compliance with the dosing schedule alone, it was unlikely that many would be able to comply with such a strict protocol. Therefore, the seroprotection rates reported reflect the seroprotection rates detectable approximately 12 months after initiation of the vaccination series. In addition, the data do indicate a trend that 2 vaccine doses given close together may not be as effective as 2 doses given further apart in time. This trend is supported by the findings of others.3 Because many of the participants in the study who received the second vaccine dose within a year received the dose 1 month after the first dose (Table 2),4 it is possible that the lower rate of seroprotection noted in the “Results” section is relatively accurate.We appreciate the interest in our paper and the opportunity to discuss this paper further.

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)0.004
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.252
Teacher spread0.234 · 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 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

Citations2
Published2002
Admission routes1
Has abstractyes

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