Evidence-based guidelines, time-based health outcomes, and the Matthew effect
Bibliographic record
Abstract
BACKGROUND: Cardiovascular risk management guidelines are 'risk based'; health economists' practice is 'time based'. The 'medical' risk-based allocation model maximises numbers of deaths prevented by targeting subjects at high risk, for example, elderly and smokers. The time-based model maximises numbers of life years gained by treating the young and non-smokers, or 'the one who has will be given more' (Matthew 25:29). We explored practical consequences of risk- or time-based allocation. METHODS: We used epidemiological modelling to generate semi-quantitative scenarios comparing the distributional effects of allocating a fixed number of prescriptions of a (hypothetical) preventive cardiovascular drug ('CVStop') either to avert the maximum number of deaths (risk-based) or to save the maximum number of life years (time based) in the male Dutch population. We subsequently asked 123 Dutch guideline developers which distribution they preferred. RESULTS: Time- and risk-based allocations resulted in different distributions of the drug across the population. There were also differences in absolute numbers of life years gained and deaths averted, and in the distribution of these across the population. For example, risk-based allocation of 'CVStop' resulted in preferential treatment of elderly, leading to more deaths averted (mostly among 70 and above) but fewer life years gained, if compared with time-based allocation. The guideline developers experienced the choice dilemmas as difficult. No priority choice was dominant among the respondents. CONCLUSION: In evidence-based resource allocation the choice to save time or to avert deaths may introduce moral choices because of the various origins of increased disease risk. Evidence-based guideline development inevitably has moral implications.
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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 | 0.143 | 0.452 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".