Incorporating future unrelated medical costs in cost-effectiveness analysis in China
Bibliographic record
Abstract
The occurrence of future unrelated medical costs is a direct consequence of life-prolonging interventions, but most pharmacoeconomic guidelines recommend the exclusion of these costs. The Chinese guidelines were updated in 2020, taking an exclusion approach for the future unrelated medical cost. We notice the research surrounding this issue continues in other countries and leads to an inclusion recommendation in some guidelines. Meanwhile, this issue has not been discussed in China, reflecting an urgent need for extensive research on its impact. We reviewed the theoretical and practical studies surrounding the inclusion of future unrelated medical costs, summarised the landscape of guidelines in other jurisdictions. We found that the inclusion would increase the internal and external consistency of economic evaluation and the comparability of results between different jurisdictions. However, more research is needed surrounding this issue. We proposed a future research agenda to inform the update of Chinese guidelines. We recommend research on individual-level healthcare reimbursement data and end-of-life costs from hospital administrative data to generate the age-specific, sex-specific and condition-specific costs. We also recommend establishing a formal process to evaluate the ethical and economic impact of including future unrelated medical costs and adjust the threshold accordingly in the guidelines.
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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 | 0.043 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".