Pneumococcal Conjugate Vaccine, Polysaccharide Vaccine, or Both for Adults? We’re Not There Yet
Bibliographic record
Abstract
There is wide acceptance that Streptococcus pneumoniae (pneumococcus) is a major cause of morbidity and mortality. With the declaration of an influenza pandemic from novel H1N1, optimizing strategies to prevent pneumococcal pneumonia and invasive disease takes on added meaning and urgency. Currently, the 23-valent pneumococcal polysaccharide vaccine (PPV23), which is licensed for use among adults and children aged >2 years, is recommended in some countries for universal use among elderly persons (defined variably) and in a larger number of countries for targeted use among nonelderly persons with specified underlying medical conditions. By contrast, the pneumococcal polysaccharide-protein conjugate vaccines (PCV)—of which 3 are now available (PCV7 [Prev(e)nar; Wyeth Vaccines], which is licensed in >100 countries, including the United States; PCV10 [Synflorix; GlaxoSmithKline], which is licensed in Canada and Europe but not in the United States); and PCV13 (Wyeth Vaccines), which is licensed in Chile—are labeled for use only in children. Their potential to overcome limitations of PPV23 and contribute to adult pneumococcal disease prevention remains unclear.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.039 | 0.017 |
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".