Validation of an Automated Mortality Index using the Electronic Medical Record System in a Network of Acute Care Hospitals
Bibliographic record
Abstract
Objective: Physicians struggle with prognostication for patients facing the final year of life. Practical tools which identify patients at the time of hospital admission who are at high risk of mortality would be helpful to provide timely access to supportive services, including palliative care and hospice. The PREDICT is a validated tool that predicts mortality risk but has not been implemented into electronic medical record (EMR) systems. The current study evaluated the validity of PREDICT within an EMR system and tracked patient mortality over 12 months.Methods: The study sample consisted of 3,488 adult patients admitted to a network of acute care hospitals. The PREDICT tool was evaluated for its ability to predict mortality within 6 and 12 months of hospitalization and was compared to the APR-DRG Mortality Risk Index (MRI).Results: A total of 299 patients (9%) were deceased within 12 months of hospital admission. Logistic regressions revealed that higher PREDICT scores were associated with greater risk of mortality within 6 and 12 months post-discharge. Receiver Operating Characteristic curve (ROC) analysis revealed that the overall PREDICT score significantly predicted mortality at 12 months (ROC = .767) and was a better predictor than the MRI.Conclusions: The PREDICT tool is a valid assessment of mortality risk and unlike the MRI, it can be readily automated in the EMR to help identify patients at greater risk of death. More research is needed to apply this tool in clinical practice and calibrate its performance across clinical settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".