Risk of invasive pneumococcal disease in children with sickle cell disease in the era of conjugate vaccines: a systematic review of the literature
Bibliographic record
Abstract
Pneumococcal conjugate vaccines (PCVs) are highly effective in preventing invasive pneumococcal diseases (IPD) in children, including those with sickle cell disease (SCD). A systematic review of the English literature published between 2000 and 2017 was undertaken to evaluate the serotype distribution, clinical presentation and outcomes of IPD in children with SCD in PCV programmes. We identified 475 potential studies and included 16 publications, involving 9438 children up to 22 years of age with SCD and 182 IPD episodes (prevalence, 1·9%. 95% confidence interval [CI], 1·7-2·2%). Septicaemia was the most prevalent clinical presentation (84/137; 61%) followed by lower respiratory tract infection (39/137; 29%) and meningitis (12/137, 9%). More than half the serotypes associated with IPD (88/148; 59·5%) were not included in the 13-valent PCV; of these, 54% (44/82) were due to serogroup 15. The crude case fatality rate was 11·5% (21/182 cases; 95% CI, 7·3-17·1%). Most cases of IPD in children with SCD were due to serotypes that are not included in any of the licensed PCVs. IPD in children with SCD remains associated with high morbidity and mortality, highlighting the importance of strict adherence to daily penicillin prophylaxis. Until a serotype-independent pneumococcal vaccine becomes available, higher-valent PCVs should include serogroup 15 to protect this highly vulnerable group of 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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".