A Pediatric Investigators Collaborative Network on Infections in Children (PICNIC) multi-centre Canadian descriptive analysis of Haemophilus influenzae bacteremia in children: Emerging serotypes
Bibliographic record
Abstract
Background: There has been dramatic reduction in Haemophilus influenzae serotype b (Hib) since introduction of Hib vaccines, but children still experience serious invasive Haemophilus influenzae (Hi) disease caused by various serotype and non-typeable bacteria.The object of this study was to describe the serotype distribution and clinical spectrum of Hi bacteremia in children admitted to Canadian hospitals.Methods: All children with Hi bacteremia admitted 2013 through 2017 to 10 centres across Canada were included.Demographic, clinical, treatment and outcome data were collected.Results: Haemophilus influenzae bacteremia occurred in 118 children of median age 12 months (inter-quartile range: 7-48 months).Forty-three (36%) isolates were non-typeable (NTHi) and 8 were not typed.Of the 67 typeable (THi), Hia (H.influenzae serotype a) (n=36, 54%), Hif (serotype f) (n=19, 26%) and Hib (serotype b) (n=9, 13%) dominated.The THi was more likely than NTHi bacteremia to present as meningitis (p<0.001),particularly serotype a (p=0.04) and less likely to present as pneumonia (p<0.001).Complicated disease (defined as intensive care unit admission, need for surgery, long-term sequelae or death) occurred in 31 (26%) cases and were more likely to have meningitis (p<0.001)than were those with uncomplicated disease. Conclusion:In the era of efficacious conjugate Hib vaccines, NTHi, Hia and Hif have emerged as the leading causes of invasive Hi in Canadian children, with Hia being most likely to result in meningitis and complicated disease.A vaccine for all NTHi and THi would be ideal, but knowledge of the current disease burden from circulating strains will inform prioritization of vaccine targets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".