A Standard Set of Value-Based Patient-Centered Outcomes and Measures of Overall Health in Adults
Bibliographic record
Abstract
BACKGROUND: The definition of population-specific outcomes is an essential precondition for the implementation of value-based health care. We developed a minimum standard outcome set for overall adult health (OAH) to facilitate the implementation of value-based health care in tracking, comparing, and improving overall health care outcomes of adults across multiple conditions, which would be of particular relevance for primary care and public health populations. METHODS: The International Consortium for Health Outcomes Measurement (ICHOM) convened an international panel (patients, clinicians, and topic experts). Following the development of a conceptual framework, a modified Delphi method (supported by public consultations) was implemented to identify, in sequence, the relevant domains, the best instruments for measuring them, the timing of measurement, and the relevant adjustment variables. FINDINGS: Outcomes were identified in relation to overall health status and the domains of physical, mental, and social health. Three instruments covering these domains were identified: PROMIS Scale v1.2-Global Health (10 items), WHO Wellbeing Index (5 items), and the WHO Disability Assessment Schedule 2.0 (12 items). Case-mix variables included a range of sociodemographic and biometric measures. Yearly measurement was proposed for all outcomes and most case-mix variables. INTERPRETATION: The ICHOM OAH Standard Set has been developed through consensus-based methods based on predefined criteria following high standards for the identification and selection of high-quality measures The involvements of a wide range of stakeholders supports the acceptability of the set, which is readily available for use and feasibility testing in clinical settings.
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 | 0.128 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".