Development and Validation of a Clinical Prediction Tool for Estimating the Risk of 1-year Mortality among Hospitalized Patients with Dementia
Bibliographic record
Abstract
Advance care planning (ACP) has an established benefit among dementia patients though may happen infrequently, which could contribute to goal-discordant end-of-life care. A prognostic tool could serve as a trigger for ACP. We sought to develop and test a clinical tool to predict the risk of 1-year mortality among hospitalized dementia patients. Population-level linked healthcare administrative databases in Ontario were used. In a cohort of 235667 patients hospitalized from 2009-2017, we developed a tool with 76 predictor variables (sociodemographic factors, comorbidities, previous interventions, functional status, nutritional status, admission-specific information, previous healthcare utilization). In a cohort of 62909 patients hospitalized from 2018-2019, the tool demonstrated acceptable discrimination (c statistic=0.796). It demonstrated acceptable calibration in the validation cohort (mean relative difference=-3.29%) and subgroups of meaning to clinicians and policy-makers. This model could be integrated into electronic medical records as an automated prognostic tool, which could prompt ACP among hospitalized dementia patients.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".