Invasive Pneumococcal Disease in High-risk Children: A 10-Year Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Despite the availability of conjugate pneumococcal vaccines, children with high-risk conditions remain vulnerable to invasive pneumococcal disease (IPD). This study sought to describe IPD prevalence, vaccination and outcomes among high-risk children. METHODS: We used International Classification of Disease10 discharge and microbiology codes to identify patients hospitalized for IPD at a large pediatric hospital from January 1, 2009, to December 31, 2018. Patients were considered high-risk if they had: primary immunodeficiency, asplenia, transplant, active malignancy, sickle cell disease, cochlear implant, nephrotic syndrome, chronic lung disease, cerebrospinal fluid leak, HIV or used immunosuppressive therapy. RESULTS: In total 94 high-risk patients were hospitalized for IPD. The most common high-risk conditions included malignancy (n = 33, 35%), solid-organ or bone marrow transplant (n = 17, 18%) and sickle cell disease (n = 14, 15%). Bacteremia was the most common presentation (n = 81, 86%) followed by pneumonia (n = 23, 25%) and meningitis (n = 9, 10%). No deaths occurred. Of 66 patients with known pneumococcal vaccination status, 15 (23%) were unvaccinated, and 51 (77%) received at least one dose of a pneumococcal vaccine; 20 received all four recommended pneumococcal conjugate vaccine (PCV) doses. Only three children received PPSV23. Of 20 children with no or partial (<3 doses) immunization, 70% (14) of IPD episodes were due to vaccine-preventable serotypes. Of 66 known IPD serotypes, 17% (n = 11) were covered by PCV13, 39% (n = 26) were covered by PPSV23 and 39% (n = 26) were nonvaccine serotype. CONCLUSIONS: Despite the availability of effective pneumococcal vaccines, IPD persists among children with high-risk conditions. Improving PCV13 and PPSV23 vaccination could significantly reduce IPD; most episodes were due to vaccine-preventable serotypes in incompletely immunized patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".