Impact on health outcome and costs of influenza treatment with oseltamivir in elderly and high-risk patients
Bibliographic record
Abstract
SummaryThe main objective of this study was to evaluate health outcomes and costs to the healthcare payer of treating influenza with oseltamivir in a high-risk population. Data from published literature, clinical trials and public sources were used to develop a decision-analytic model simulating a high-risk population in the UK. The underlying clinical pathway predicts morbidity and mortality due to influenza, and its specified complications for the two influenza treatment strategies—oseltamivir and usual care. Health outcomes (quality-adjusted life years [QALYs], days to return to normal activity) and costs were estimated for events in the model. Robustness of the results was tested by probabilistic, univariate and multivariate sensitivity analyses.Treatment with oseltamivir within 48 hours results in reduced morbidity, which translates into faster recovery and return to normal activity. Economic evaluation showed that treatment with oseltamivir in a high-risk population in the UK is a cost-effective strategy in all analysed scenarios with cost-utility ratios between £225 and £17,900 per QALY gained.Treatment with oseltamivir is effective in terms of health outcome and cost for high-risk patients from the perspectives of the individual patient and healthcare payer.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".