Clinical Presentations and Outcomes of Children in Canada With Recurrent Invasive Pneumococcal Disease From the IMPACT Surveillance Network
Bibliographic record
Abstract
BACKGROUND: Invasive pneumococcal disease due to Streptococcus pneumoniae can cause mortality and severe morbidity due to sepsis, meningitis and pneumonia, particularly in young children and the elderly. Recurrent invasive pneumococcal disease is rare yet serious sequelae of invasive pneumococcal disease that is associated with the immunocompromised and leads to a high mortality rate. METHOD: This retrospective study reviewed recurrent invasive pneumococcal disease cases from the Canadian Immunization Monitoring Program, ACTive (IMPACT) between 1991 and 2019, an active network for surveillance of vaccine-preventable diseases and adverse events following immunization for children ages 0-16 years. Data were collected from 12 pediatric tertiary care hospitals across all 3 eras of public pneumococcal conjugate vaccine implementation in Canada. RESULTS: The survival rate within our cohort of 180 recurrent invasive pneumococcal disease cases was 98.3%. A decrease of 26.4% in recurrent invasive pneumococcal disease due to vaccine serotypes was observed with pneumococcal vaccine introduction. There was also a 69.0% increase in the rate of vaccination in children with preexisting medical conditions compared with their healthy peers. CONCLUSION: The decrease in recurrent invasive pneumococcal disease due to vaccine-covered serotypes has been offset by an increase of non-vaccine serotypes in this sample of Canadian children.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| 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".