Defining Multimorbidity in Older Surgical Patients
Bibliographic record
Abstract
BACKGROUND: There are numerous definitions of multimorbidity (MM). None systematically examines specific comorbidity combinations accounting for multiple testing when exploring large datasets. OBJECTIVES: Develop and validate a list of all single, double, and triple comorbidity combinations, with each individual qualifying comorbidity set (QCS) more than doubling the odds of mortality versus its reference population. Patients with at least 1 QCS were defined as having MM. RESEARCH DESIGN: Cohort-based study with a matching validation study. SUBJECTS: All fee-for-service Medicare patients between age 65 and 85 without dementia or metastatic solid tumors undergoing general surgery in 2009-2010, and an additional 2011-2013 dataset. MEASURES: 30-day all-location mortality. RESULTS: There were 576 QCSs (2 singles, 63 doubles, and 511 triples), each set more than doubling the odds of dying. In 2011, 36% of eligible patients had MM. As a group, multimorbid patients (mortality rate=7.0%) had a mortality Mantel-Haenszel odds ratio=1.90 (1.77-2.04) versus a reference that included both multimorbid and nonmultimorbid patients (mortality rate=3.3%), and Mantel-Haenszel odds ratio=3.72 (3.51-3.94) versus only nonmultimorbid patients (mortality rate=1.6%). When matching 3151 pairs of multimorbid patients from low-volume hospitals to similar patients in high-volume hospitals, the mortality rates were 6.7% versus 5.2%, respectively (P=0.006). CONCLUSIONS: A list of QCSs identified a third of older patients undergoing general surgery that had greatly elevated mortality. These sets can be used to identify vulnerable patients and the specific combinations of comorbidities that make them susceptible to poor outcomes.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".