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Record W4413435130 · doi:10.1097/mop.0000000000001486

Resurgence of pertussis: whopping the ‘100-day cough’

2025· article· en· W4413435130 on OpenAlexaff
C D C Christie

Bibliographic record

VenueCurrent Opinion in Pediatrics · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBacterial Infections and Vaccines
Canadian institutionsChild, Adolescent and Family Mental Health
Fundersnot available
KeywordsMedicineWhooping coughAnesthesiaVirology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Against the WHO's report of 84% diphtheria-pertussis-tetanus (DPT) primary vaccination coverage globally, the resurgence of pertussis (whooping cough), contributing factors and measures to control it are described. RECENT FINDINGS: USA and China, with 94-97% primary DPT immunization uptake, reported a 6-fold and 65-fold increase in pertussis between two time periods in 2023 and 2024. The global post-COVID-19 pertussis epidemic is trending towards a shift from infants towards older persons. Macrolide resistance is prevalent in 98% of Bordetella pertussis strains in China and is now reported from other countries. Pertactin-deficient mutant acellular pertussis vaccine-evasive strains are now transmitted in older children and adults. Pertactin-producing B. pertussis is causing fulminant pertussis in newborns whose mothers were not immunized in pregnancy and in under-immunized infants. Circulating epidemic strains of B. pertussis were discordant to those contained in whole-cell (Bp137) pertussis vaccine. The pertussis resurgence maybe explained by increased case ascertainment and reporting, mutant B. pertussis strains with immune escape from acellular and whole cell vaccines, and/or macrolides, waning natural, or vaccine-induced immunity and COVID-19 pandemic factors. SUMMARY: Pertussis maybe curtailed with public education, active clinical and microbiological surveillance, appropriate antimicrobial treatment and prophylaxis, public health reporting, infection control and optimized immunizations to reduce attributable morbidity and mortality.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.041
GPT teacher head0.334
Teacher spread0.292 · 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 designNot applicable
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

Citations3
Published2025
Admission routes1
Has abstractyes

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