Discordant immunization schedules can complicate vaccine evaluation for Europe
Bibliographic record
Abstract
Background: Most of the world’s vaccines are produced in Europe. Although vaccine licensure can becentralized through the EMEA, immunization recommendations are established at the national levels,reimbursement policies vary widely (ranging from regional to national, from private to public) and the lag timecan be long between licensure and eventual introduction into a national immunization program.Methods: An example of this discordance is the paediatric combination vaccines. Young infants in some EUcountries receive a whole-cell pertussis vaccine, in a three- to five-vaccine combination (“DTPw | IPV | Hib”).Acellular pertussis vaccines have been introduced over the last decade in many other EU countries, with four-to six-vaccine combinations (“DTPa | IPV | HBV | Hib”). Either of these combinations may be administered witha “3 + 1” schedule, with the first dose given between the age of 2 to 3 months, a spacing of 1 to 2 monthsbetween doses, and the final (booster) dose usually given at anywhere between 12 and 24 months of age, butin a handful of countries as late as the age of 3 to 5 years. By contrast, a “2 + 1” schedule is applied in somecountries for the “DTPa | IPV | Hib” or “DTPa | IPV | HBV | Hib” vaccines: first dose, 3 months old; spacing, 2months between doses; final (booster) dose, 11 to 14 months of age.Results: Differing national policies in the EU may have led to delays in the introduction of the newest vaccines(e.g., pneumococcal conjugate, meningococcal conjugate, rotavirus, influenza, varicella-zoster, etc.) thatmust be shown to be compatible with the various infant immunization programs across Europe. This coulddelay the likelihood, in some EU countries, of the public health advancements that these new vaccines canprovide.Conclusions: Sharing of best practices from vaccination schedules might rationalize vaccine development,streamline the introduction of novel vaccines into the national immunization programs, and facilitate theevaluation of the impact of new vaccines in Europe.
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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 | 0.360 | 0.422 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".