Discrimination and Calibration of Clinical Prediction Models
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Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.079 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Accurate information regarding prognosis is fundamental to optimal clinical care. The best approach to assess patient prognosis relies on prediction models that simultaneously consider a number of prognostic factors and provide an estimate of patients' absolute risk of an event. Such prediction models should be characterized by adequately discriminating between patients who will have an event and those who will not and by adequate calibration ensuring accurate prediction of absolute risk. This Users' Guide will help clinicians understand the available metrics for assessing discrimination, calibration, and the relative performance of different prediction models. This article complements existing Users' Guides that address the development and validation of prediction models. Together, these guides will help clinicians to make optimal use of existing prediction models.
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The record
- Venue
- JAMA
- Topic
- Health Systems, Economic Evaluations, Quality of Life
- Field
- Economics, Econometrics and Finance
- Canadian institutions
- McMaster UniversityToronto General HospitalImpactUniversity Health Network
- Funders
- —
- Keywords
- MedicineCalibrationEvent (particle physics)Predictive modellingMachine learningData miningArtificial intelligenceMedical physicsStatisticsComputer science
- Has abstract in OpenAlex
- yes