Exploring the Utility of the Modified Hospitalized‐Patient One‐Year Mortality Risk Score to Trigger Referrals to Palliative Care for Inpatients With Cancer
Bibliographic record
Abstract
BACKGROUND: Estimating prognosis can be a barrier to timely palliative care involvement. The modified Hospitalized-patient One-year Mortality Risk (mHOMR) score uses hospital admission data to calculate the risk of death within 12 months and may be a useful tool to trigger a referral to palliative care. METHODS: The mHOMR tool was retrospectively applied to consecutive acute admissions to a quaternary cancer center in Toronto, Canada from March 1 to May 31, 2018. The study aimed to investigate the association between dichotomized mHOMR scores (the cohort median score of 0.27 and the developer-recommended score of 0.21) and the risk of death, and whether these could be used to identify patients who may benefit from timely palliative care involvement. RESULTS: Of 269 inpatients, 87 were elective admissions and excluded from further analyses. At the median mHOMR score of 0.27, 91/182 patients (50%) were categorized as high-risk of death within 12 months (mHOMR+), 53 (58%) were referred to palliative care. At the lower cut-off of 0.21, 103 patients were mHOMR+, of whom 57 (55.3%) were referred to palliative care. The higher mHOMR was significantly associated with mortality (29.7% mHOMR- vs. 39.8% mHOMR+ at 12 months, log-rank p < 0.05). The association between the developer-recommended mHOMR cut-off (≥ 0.21) and mortality was not significant (p = 0.15). CONCLUSIONS: A higher mHOMR score was significantly associated with the risk of mortality in patients with advanced cancer. However, the developer-recommended mHOMR cut-off of 0.21 failed to identify a statistically significant difference between patients with advanced cancer at low versus high scores. While mHOMR may be a useful tool to augment clinical judgment and identify inpatients with advanced cancer at high risk of death, who in turn may benefit from referral to palliative care, the optimal mHOMR cutoff may warrant adjustment for this population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".