mHOMR: a prospective observational study of an automated mortality prediction model to identify patients with unmet palliative needs
Bibliographic record
Abstract
OBJECTIVE: Identification of patients with shortened life expectancy is a major obstacle to delivering palliative/end-of-life care. We previously developed the modified Hospitalised-patient One-year Mortality Risk (mHOMR) model for the automated identification of patients with an elevated 1-year mortality risk. Our goal was to investigate whether patients identified by mHOMR at high risk for mortality in the next year also have unmet palliative needs. METHOD: We conducted a prospective observational study at two quaternary healthcare facilities in Toronto, Canada, with patients admitted to general internal medicine service and identified by mHOMR to have an expected 1-year mortality risk of 10% or more. We measured patients' unmet palliative needs-a severe uncontrolled symptom on the Edmonton Symptom Assessment Scale or readiness to engage in advance care planning (ACP) based on Sudore's ACP Engagement Survey. RESULTS: Of 518 patients identified by mHOMR, 403 (78%) patients consented to participate; 87% of those had either a severe uncontrolled symptom or readiness to engage in ACP, and 44% had both. Patients represented frailty (38%), cancer (28%) and organ failure (28%) trajectories were admitted for a median of 6 days, and 94% survived to discharge. CONCLUSIONS: A large majority of hospitalised patients identified by mHOMR have unmet palliative needs, regardless of disease, and are identified early enough in their disease course that they may benefit from a palliative approach to their care. Adoption of such a model could improve the timely introduction of a palliative approach for patients, especially those with non-cancer illness.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".