Is there a need for pneumococcal vaccination programs for the homeless to prevent invasive pneumococcal disease?
Bibliographic record
Abstract
Introduction Homeless are a vulnerable segment of society at risk for infections. Invasive pneumococcal disease (IPD) infections are increasingly identified in homeless as a major contributor of morbidity and mortality. Very few countries have recommended pneumococcal vaccination programs for the homeless.Areas covered A literature review of IPD in the homeless from 1980 to 2020 in PubMed and Scopus using terms, ‘homeless,’ and ‘homelessness,’ and after ‘chronic’ or ‘transitory’ homeless was conducted. Gray literature from the CDC, WHO and major governmental agencies/organizations were included. Terms for IPD eligible for inclusion in the database search included ‘pneumococcal disease,’ ‘invasive pneumococcal disease,’ ‘Streptococcus pneumoniae.’ This included outbreaks of pneumococcal disease in the homeless, clinical characteristics associated with pneumococcal disease in the homeless, pneumococcal vaccines and vaccination recommendations for the homeless.Expert opinion Homeless populations are at increased risk for IPD in comparison to the general population. This is due to clinical conditions experienced by the homeless including alcoholism and cigarette smoking. In addition, a collection of specific pneumococcal serotypes are associated with IPD in this group. Pneumococcal vaccine programs aimed at this population would likely decrease the incidence of IPD for this group and contribute to better overall health for the homeless.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".