Pneumococcal serotypes missing prespecified efficacy threshold in immunogenicity trials: real-world evidence from national immunization programs
Bibliographic record
Abstract
The 13-valent (PCV13) and 10-valent (PCV10) pneumococcal conjugate vaccines missed non-inferiority for certain 7-valent (PCV7) serotypes in immunogenicity trials. This study examines the population-level IPD case trends for these serotypes. We identified six countries with national IPD surveillance data that introduced PCV13 (Canada, Germany, Israel, Italy, South Africa, and the United States) and three with PCV10 (Finland, Brazil, and the Netherlands). We extracted country-specific annual IPD case counts for serotypes that met non-inferiority (4, 14, 18C, 19F, 9 V [PCV10 only]) and missed non-inferiority (6B, 23F, 9 V [PCV13 only]) for different age groups (<5 and ≥5 years) for eight years following PCV13/PCV10 introduction. For all ages and countries, IPD cases due to PCV7 serotypes that missed non-inferiority either decreased or remained suppressed following PCV13/PCV10 introduction. Similar trends were found for PCV7 serotypes that met non-inferiority in those <5 years. Paradoxically, cases increased in those ≥5 years in Canada, Italy, and the US, primarily driven by increases in serotypes 4 and 19F disease. Despite missing non-inferiority for serotypes in immunogenicity trials, higher-valent PCVs effectively suppressed these serotypes across all ages. Non-inferiority criteria from immunogenicity trials may not fully predict real-world disease impact after PCV implementation.
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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 | 0.148 | 0.316 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".