Collaboration on a public health-driven vaccine initiative
Bibliographic record
Abstract
Disease surveillance can be used as an opportunity to determine priorities for research and the development of new therapeutics.This is evident in the work underway to develop a new vaccine to combat a serious invasive childhood disease: Haemophilus influenzae serotype a (Hia).Following the introduction of Hib vaccine into the routine childhood immunization schedule in Canada in the early 1990's, the Public Health Agency of Canada (PHAC) began to document the dropping rates of H influenzae serotype b (Hib) infection.However, invasive H. influenzae diseases due to non-Hib strains began to increase and in 2007, surveillance for invasive H. influenzae disease due to all serotypes as well as non-typeable strains was initiated.Current data suggests Hia is a cause of serious invasive disease, particularly in Aboriginal populations.Similar to Hib, Hia causes severe illnesses such as meningitis, sepsis and bacteremic pneumonia in young children under the age of five.Given the emerging threat due to Hia in Aboriginal populations in Canada, PHAC formed a partnership with the National Research Council of Canada (NRC) to investigate the potential of creating a capsular polysaccharide vaccine against Hia.At the present time, candidate vaccine seed strains have been identified and PHAC and the NRC are working with the Northern Ontario School of Medicine, the US Centers for Disease Control and Prevention and others.The goal of this research is to identify and prepare a candidate vaccine against Hia while increasing the understanding of how such a vaccine would improve the health of a vulnerable population.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.049 | 0.009 |
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".