Clusters of multimorbidity across hospital services and by language groups
Bibliographic record
Abstract
Objective: Documenting multimorbidity profiles and resource use across hospital sectors can help inform and improve healthcare delivery. The purpose of this cohort study (2013-2017) was to describe profiles of multimorbidity among patients at an acute care hospital in Ontario, Canada.Methods: This was a retrospective cohort study over five fiscal years. Data from patients who were admitted as inpatients, visited the emergency department (ED), or received day surgeries at an acute care hospital in Ottawa, Canada between 2013 and 2017 were obtained from two individual-level administrative databases. Diagnoses for 13 chronic diseases and clusters of multimorbidity were identified using validated methods. The analysis sample was comprised of 22,932 patients with multimorbidity aged 18 years or over. Demographic (e.g., age) and clinical (e.g., ED visit count) characteristics of chronic disease clusters were examined across inpatient, ED, and day surgery services, and between language groups.Results: The most common disease profiles encompassed hypertension, diabetes, and arthritis. Mental health and mood conditions were highly concomitant among ED patients. Degree of multimorbidity was significantly associated with length of stay (LOS) and frequency of ED visits. Compared to Anglophone inpatients, hospitalized Francophone patients had significantly more comorbid conditions.Conclusions: Treatment plans should be tailored for different types of hospital services and will need to be patient-centered to account for variability in disease clusters, sociodemographic factors, and acuity levels. More studies are needed to understand the impacts of multimorbidity on healthcare systems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".