Prognosticating with the Hospitalized‐patient One‐year Mortality Risk Score Using Information Abstracted from the Medical Record
Bibliographic record
Abstract
BACKGROUND: Predicting death risk in patients with diverse conditions is difficult. The Hospitalized-patient One-year Mortality Risk (HOMR) score accurately determines death risk in adults admitted to hospital using health administrative data unavailable to clinicians and most researchers. OBJECTIVE: Determine if HOMR is valid when calculated using data abstracted directly from the medical record. DESIGN: Medical record review linked to population-based administrative data. PARTICIPANTS: 4996 adults admitted in 2011 to a nonpsychiatric service at a tertiary hospital. MAIN MEASURES: From the chart, we abstracted information required to calculate the HOMR score and linked to population-based mortality data to determine vital status within 1 year of admission date. KEY RESULTS: Patients had a mean age of 55.6 (standard deviation [SD], 20.7) with 563 (11.3%) dying. The mean chart HOMR score was 22 (SD, 12) and significantly predicted death risk; a 1-point increase in HOMR increased death odds by 19% (odds ratio, 1.192;, 95% confidence interval [CI], 1.175-1.210;, P < 0.0001). Chart HOMR was strongly discriminative ( C statistic 0.888) and well calibrated (Hosmer-Lemeshow goodness-of-fit test, 12.9; P = 0.11). The observed death risk was strongly associated with expected death risk (calibration slope, 1.02; 95% CI, 0.89-1.16). Notation of delirium or falls on admitting notes or dependence for at least 1 activity of daily living were each associated with 1-year death risk independent of the HOMR score. CONCLUSIONS: One-year mortality risk can be accurately determined in adults admitted to hospital with the HOMR score calculated using information abstracted from the medical record. Patient functional status was independently associated with death risk. Journal of Hospital Medicine 2017;12:224-230.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".