Bibliographic record
Abstract
In industrialised countries, the highest incidence of meningococcal disease is observed in young children, while colonisation is most common in teenagers and young adults. The prevalence of meningococcal carriage is a poor predictor of disease risk because other factors, including host susceptibility and the invasive potential of the organism, are important. The natural history of meningococcal infection is dominated by transmission between carriers and disease is a relatively rare event. As the experience with meningococcal serogroup C conjugate (MCC) vaccines illustrates, vaccines that can influence transmission in addition to disease will have much greater population impact. In the UK, excellent control of serogroup C disease continues, largely because of sustained herd immunity. The ability of other meningococcal conjugate and candidate protein vaccines to reduce carriage, and indeed the effect of MCC vaccines in different epidemiological contexts, is not known, Given the potential magnitude of herd immunity that can be achieved, carriage studies should be considered as an important component of vaccine evaluation. Outcomes of interest include the prevalence of carriage before and after immunisation. This information is highly relevant in terms of optimising vaccine strategy and determining the likely cost-effectiveness of immunisation programmes. For example, if a vaccine has no effect on carriage, routine infant immunisation may be preferred, whereas for a vaccine that is able to prevent carriage, strategies that include a catch-up campaign and target the age-group which is driving transmission may be much more attractive.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".