Developing a vaccine for Haemophilus influenzae serotype a: Proceedings of a workshop
Bibliographic record
Abstract
Since the late 1990s there has been an emergence of Haemophilus influenzae serotype a (Hia) infections, especially in Indigenous communities in the northern regions of Canada and Alaska associated with significant morbidity and approximately a 10% mortality.A Hia vaccine could potentially prevent this disease and save the health care system millions of dollars in both acute and long-term care.On March 23-24, 2016, the National Research Council (NRC), the Public Health Agency of Canada (PHAC) and the Canadian Institutes of Health Research (CIHR) co-organized a meeting on H. influenzae serotype a (Hia) to examine the current state of disease epidemiology and a potential vaccine solution path.The meeting included representatives from academia, federal and territorial public health units, hospital laboratories, federal departments involved in Aboriginal health, advocacy organizations for Indigenous peoples and industry.Representatives from industry confirmed having the capacity and the interest to support preparation of clinical trial batches.Canadian regulatory authorities have expressed a willingness to help ensure appropriate measures are in place for licensure purposes.Furthermore, there is the capacity and interest in performing some clinical trials in Indigenous communities in both Canada and Alaska.Recommendations for next steps included: complete pre-clinical studies, improve epidemiological surveillance to better understand the extent of the disease in the rest of North America and globally, establish engagement mechanisms with national Indigenous organizations to ensure their peoples are fully involved in the process and explore funding opportunities to prepare clinical lots and undertake clinical trials.
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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".