Bibliographic record
Abstract
PURPOSE OF REVIEW: To discuss recent findings on the global burden of respiratory tract infections in underprivileged populations, highlighting the critical role of socioeconomic factors in the incidence and severity of these diseases, with a particular focus on health disparities affecting Indigenous communities. RECENT FINDINGS: Pulmonary tuberculosis and lower respiratory tract infections, particularly those caused by Streptococcus pneumoniae and respiratory syncytial virus (RSV), continue to disproportionally impact populations in low-income countries and Indigenous communities worldwide. Indigenous children <5 years old bear the highest global burden of RSV infection, reflecting persistent social inequalities between Indigenous and non-Indigenous populations. Repeated episodes of acute pneumonia during childhood significantly contribute to the high prevalence of chronic respiratory diseases among Indigenous populations. The widespread occurrence of bronchiectasis in these communities is closely linked to adverse socioeconomic conditions. SUMMARY: Significant disparities in the incidence and severity of lower respiratory tract infections between affluent and impoverished populations are driven by socioeconomic inequalities. Vaccinating vulnerable population groups with newly developed vaccines has the potential to prevent infections caused by pathogens such as S. pneumoniae and RSV. However, global access to these vaccines and monoclonal antibodies remains limited due to their high costs.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".