Prevalence of <i>Haemophilus influenzae</i> in the nasopharynx of children from regions with varying incidence of invasive <i>H. influenzae</i> serotype a disease: Canadian Immunization Research Network (CIRN) study
Bibliographic record
Abstract
Haemophilus influenzae serotype a (Hia) has recently emerged as an important cause of invasive disease in the North American Arctic and Sub-Arctic regions, mainly affecting young Indigenous children. In this study, we addressed the question of whether the prevalence of Hia and all H. influenzae in the nasopharynx differed between paediatric populations from regions with high versus low incidence of invasive Hia disease. Nasopharyngeal specimens from children with acute respiratory tract infections (ARTI) collected for routine diagnostic detection of respiratory viruses were analysed with molecular-genetic methods to identify and serotype H. influenzae. In Nunavut, a region with a high incidence of invasive Hia disease, all H. influenzae and particularly Hia were found in the nasopharynx of 60.6% and 3.0% children. In Southern Ontario (Hamilton region), where Hia invasive disease is rare, the frequencies of all H. influenzae and Hia detection were 38.5% and 0.6%, respectively. In both cohorts, non-typeable H. influenzae was prevalent (57.0% and 37.9%, respectively). Considering that Hia is an important cause of severe invasive disease in Nunavut children, 3% prevalence of Hia among children with ARTI can reflect continuing circulation of the pathogen in the Northern communities that may result in invasive disease outbreaks.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".