Evaluating the impact of RSV immunisation strategies on antibiotic use in England
Bibliographic record
Abstract
Abstract With a sizable fraction of primary care antibiotics prescribing attributable to RSV, the promising suite of prophylactic pharmaceuticals against could reduce the need for antibiotics in addition to controlling respiratory disease. To assess the potential impact of RSV vaccines on the reduction in primary care antibiotic prescribing in England, we integrate results from a dynamic transmission model of RSV and a statistical attribution framework. Under base case assumptions, targeting children aged 5-14 years reduces antibiotic prescribing by 10.9 (8.0-14.2) antibiotic courses per 10,000 person years. This reduction in antibiotic use would gain 128 DALYs and avert 51,000 GBP associated with infections caused by drug resistant bacteria. Seasonally administering monoclonal antibodies (mAbs) to high risk infants under 6 months is the most efficient strategy (reducing per person year antibiotic prescribing by 2.6 (1.9-3.3) antibiotic courses per 1,000 mAb courses).
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".