Identifying the research, advocacy, policy and implementation needs for the prevention and management of respiratory syncytial virus lower respiratory tract infection in low- and middle-income countries
Bibliographic record
Abstract
The high burden of respiratory syncytial virus (RSV) infection in young children disproportionately occurs in low- and middle-income countries (LMICs). The PROUD (Preventing RespiratOry syncytial virUs in unDerdeveloped countries) Taskforce of 24 RSV worldwide experts assessed key needs for RSV prevention in LMICs, including vaccine and newer preventive measures.A global, survey-based study was undertaken in 2021. An online questionnaire was developed following three meetings of the Taskforce panellists wherein factors related to RSV infection, its prevention and management were identified using iterative questioning. Each factor was scored, by non-panellists interested in RSV, on a scale of zero (very-low-relevance) to 100 (very-high-relevance) within two scenarios: (1) Current and (2) Future expectations for RSV management.Ninety questionnaires were completed: 70 by respondents (71.4% physicians; 27.1% researchers/scientists) from 16 LMICs and 20 from nine high-income (HI) countries (90.0% physicians; 5.0% researchers/scientists), as a reference group. Within LMICs, RSV awareness was perceived to be low, and management was not prioritised. Of the 100 factors scored, those related to improved diagnosis particularly access to affordable point-of-care diagnostics, disease burden data generation, clinical and general education, prompt access to new interventions, and engagement with policymakers/payers were identified of paramount importance. There was a strong need for clinical education and local data generation in the lowest economies, whereas upper-middle income countries were more closely aligned with HI countries in terms of current RSV service provision.Seven key actions for improving RSV prevention and management in LMICs are proposed.
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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 | 0.037 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".