Gaps in knowledge about the vaccine coverage of immunocompromised children: a scoping review
Bibliographic record
Abstract
Immunocompromised children are at increased risk of severe illness from vaccine-preventable infections. However, inadequate vaccine coverage remains a concern. This scoping review sought to determine the current state of knowledge regarding vaccine coverage of immunocompromised children. Bibliographic databases were searched for primary research from any year. Data were analyzed quantitatively and narratively. Ninety-seven studies met inclusion criteria. The most commonly studied vaccines were pneumococcal (n = 46), influenza (n = 44), diphtheria/tetanus/pertussis/poliomyelitis/Haemophilus influenzae type B/hepatitis B-containing (n = 36), and measles- and/or mumps- and/or rubella-containing (n = 29). Immunocompromising conditions studied included cancer/stem cell transplants (n = 24), solid organ transplants (n = 23), sickle cell disease (n = 21), immunosuppressive therapy (n = 14), human immunodeficiency virus (n = 12), splenectomy (n = 4), and primary immunodeficiency (n = 2). As more children are treated with immunosuppressive therapies, it is critical to identify whether they are being appropriately vaccinated for age and condition. We identified gaps in the current state of knowledge for specific vaccine types in specific immunocompromised populations.
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) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".