Bibliographic record
Abstract
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.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
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".