Epidemiology of invasive <i>Haemophilus influenzae</i> disease in northwestern Ontario: comparison of invasive and noninvasive <i>H. influenzae</i> clinical isolates
Bibliographic record
Abstract
In the post- Haemophilus influenzae type b (Hib) vaccine era, invasive H. influenzae type a (Hia) disease emerged in North American Indigenous populations. The role of Hia in noninvasive disease is uncertain; it is unknown whether noninvasive Hia infections are prevalent in populations with a high incidence of invasive disease, and whether invasive and noninvasive Hia isolates have different characteristics. We analyzed all invasive and noninvasive clinical H. influenzae isolates collected in a northwestern Ontario hospital serving 82% Indigenous population over 5.5 years. Serotyping, clonal analysis, and antimicrobial sensitivity testing were conducted on 233 noninvasive and 20 invasive isolates. Among noninvasive isolates, 91% were nontypeable (NTHi) and 3% were Hia; Hia was the most frequent invasive isolate (60%). Incidence rates of invasive H. influenzae disease (12.5/100 000/year) greatly exceeded average provincial data, with the highest found in <6-year-old children (63.9/100 000/year); the proportion of Hia among invasive isolates was seven times larger than in Ontario. No difference in clonal characteristics between invasive and noninvasive Hia isolates was found. Antibiotic resistance was more common among NTHi than among encapsulated isolates, without differences between invasive and noninvasive isolates. Considering the significance of Hia in Indigenous populations, pediatric immunization against Hia will be useful to prevent serious infections in young Indigenous children.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".