Estimates for quality of life loss due to Respiratory Syncytial Virus
Bibliographic record
Abstract
Abstract Background In children aged <5 years in whom severe respiratory syncytial virus (RSV) episodes predominantly occur, there are currently no appropriate standardised instruments to estimate quality of life years (QALY) loss. Objectives We estimated the age‐specific QALY loss due to RSV by developing a regression model which predicts the QALY loss without the use of standardised instruments. Methods We conducted a surveillance study which targeted confirmed RSV episodes in children aged <5 years (confirmed cases) and their household members who experienced symptoms of RSV during the same time (suspected cases). All participants were asked to complete questions regarding their health during the infection, with the suspected cases additionally providing health‐related quality of life (HR‐QoL) loss estimates by completing EQ‐5D‐3L‐Y or EQ‐5D‐3L instruments. We used the responses from the suspected cases to calibrate a regression model which estimates the HR‐QoL and QALY loss due to infection. Findings For confirmed RSV cases in children under 5 years of age who sought health care, our model predicted a QALY loss per RSV episode of 3.823 × 10−3 (95% CI 0.492‐12.766 × 10−3), compared with 3.024 × 10−3 (95% CI 0.329‐10.098 × 10−3) for under fives who did not seek health care. Quality of life years loss per episode was less for older children and adults, estimated as 1.950 × 10−3 (0.185‐9.578 × 10−3) and 1.543 × 10−3 (0.136‐6.406 × 10−3) for those who seek or do not seek health care, respectively. Conclusion Evaluations of potential RSV vaccination programmes should consider their impact across the whole population, not just young child children.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".