Don’t Discount Societal Value in Cost-Effectiveness Comment on "Priority Setting for Universal Health Coverage: We Need Evidence-Informed Deliberative Processes, Not Just More Evidence on Cost-Effectiveness"
Bibliographic record
Abstract
As healthcare resources become increasingly scarce due to growing demand and stagnating budgets, the need for effective priority setting and resource allocation will become ever more critical to providing sustainable care to patients. While societal values should certainly play a part in guiding these processes, the methodology used to capture these values need not necessarily be limited to multi-criterion decision analysis (MCDA)-based processes including 'evidence-informed deliberative processes.' However, if decision-makers intend to not only incorporates the values of the public they serve into decisions but have the decisions enacted as well, consideration should be given to more direct involvement of stakeholders. Based on the examples provided by Baltussen et al, MCDA-based processes like 'evidence-informed deliberative processes' could be one way of achieving this laudable goal.
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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 | 0.017 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.109 | 0.094 |
| Insufficient payload (model declined to judge) | 0.010 | 0.011 |
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".