Predictive risk algorithms in a population setting: an overview
Bibliographic record
Abstract
BACKGROUND: The widespread use of risk algorithms in clinical medicine is testimony to how they have helped transform clinical decision-making. Risk algorithms have a similar but underdeveloped potential to support decision-making for population health. OBJECTIVE: To describe the role of predictive risk algorithms in a population setting. METHODS: First, predictive risk algorithms and how clinicians use them are described. Second, the population uses of risk algorithms are described, highlighting the strengths of risk algorithms for health planning. Lastly, the way in which predictive risk algorithms are developed is discussed briefly and a guide for algorithm assessment in population health presented. CONCLUSION: For the past 20 years, absolute and baseline risk has been a cornerstone of population health planning. The most accurate and discriminating method to generate such estimates is the use of multivariable risk algorithms. Routinely collected data can be used to develop algorithms with characteristics that are well suited to health planning and such data are increasingly available. The widespread use of risk algorithms in clinical medicine is testimony to how they have helped transform clinical decision-making. Risk algorithms have a similar but underdeveloped potential to support decision-making for population health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".