Population-based epidemiology of invasive pneumococcal infection in children in nine urban centers in Canada, 1994 through 1998
Bibliographic record
Abstract
PURPOSE: To describe the epidemiology of invasive pneumococcal infections in Canadian children 0 to 12 years old. METHODS: At each of nine urban centers, active surveillance was conducted to identify all cases of invasive pneumococcal infection in children during 1994 to 1998. Postal codes were used to distinguish cases resident in defined urban areas from referral cases. Census data were obtained for each defined area to calculate age-specific incidence rates. Features of population-based cases were described. RESULTS: From an average defined population of approximately 1 million children, 937 eligible cases arose. Those 6 to 17 months old had the highest average incidence rate of 98.6/100 000/year. The average cumulative risk of infection was 1 in 460 between birth and 59 months, by which age 92% of cases had occurred. Among cases younger than 2 years of age, simple bacteremia accounted for 66%, pneumonia with bacteremia accounted for 14.7% and meningitis accounted for 11% (average incidence rate, 9.0/100 000/year). An underlying illness was present in 16% of all cases. The mortality rate was 1.8%. CONCLUSIONS: Invasive pneumococcal infections are relatively common in early childhood, based on 5 years of data from nearly 20% of the Canadian population ages 0 to 12 years. These data will be valuable for calculating the economic case for universal infant vaccination with newly available vaccines.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".