Bibliographic record
Abstract
Rotaviruses are the foremost cause of severe gastroenteritis in young children, being responsible for 600,000 deaths, 2 million hospitalizations, 25 million clinic visits, 111 million episodes and 56% of hospitalizations for febrile gastroenteritis worldwide, each year. Every child is infected by age five years. Rotateq™ is an oral, ready-to-use, 3-dose regimen vaccine, containing 5 human-bovine reassortant rotavirus serotypes given at 2, 4, 6 months of age, that is easily integrated into pre-established immunization schedules. The product has been studied in over 70,000 infants from all five continents. Rotateq™ has proven efficacy of 98% against severe gastroenteritis for G1-4 strains, was well tolerated, including with respect to intussusception in prelicensure and postmarketing surveillance, with no increase in fever, irritability, or hematochezia, while it reduced health care contacts for rotaviruses by nearly 100%. Rotateq™ has FDA-approval and is now in use in 70 countries, with application pending in another 75 countries, worldwide. RotateqTM is now widely available in USA, Canada, Australia, eleven countries in Europe, Latin America, the Caribbean and also in other parts of the world. RotateqTM is now in the review process for WHO-prequalification. Given the universal nature of rotavirus gastroenteritis, this vaccine is an extremely important public health priority.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".