Integrating the patient voice: patient-centred and equitable clinical risk prediction for kidney health and disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: Personalized approaches to care are increasingly common in clinical nephrology. Although risk prediction models are developed to estimate the risk of kidney-disease related outcomes, they infrequently consider the priorities of patients they are designed to help. RECENT FINDINGS: This review discusses certain steps in risk prediction tool development where patients and their priorities can be incorporated. Considering principles of equity throughout the process has been the focus of recent literature. SUMMARY: Applying a person-centred lens has implications for several aspects of risk prediction research. Incorporating the patient voice may involve partnering with patients as researchers to identify the target outcome for the tool and/or determine priorities for outcomes related to the kidney disease domain of interest. Assessing the list of candidate predictors for associations with inequity is important to ensure the tool will not widen disparity for marginalized groups. Estimating model performance using person-centred measures such as model calibration may be used to compare models and select a tool more useful to inform individual treatment decisions. Finally, there is potential to include patients and families in determining other elements of the prediction framework and implementing the tool once development is complete.
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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 | 0.021 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".