Patient characteristics, resource use and outcomes associated with general internal medicine hospital care: the General Medicine Inpatient Initiative (GEMINI) retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: The precise scope of hospital care delivered under general internal medicine services remains poorly quantified. The purpose of this study was to describe the demographic characteristics, medical conditions, health outcomes and resource use of patients admitted to general internal medicine at 7 hospital sites in the Greater Toronto Area. METHODS: This was a retrospective cohort study involving all patients who were admitted to or discharged from general internal medicine at the study sites between Apr. 1, 2010, and Mar. 31, 2015. Clinical data from hospital electronic information systems were linked to administrative data from each hospital. We examined trends in resource use and patient characteristics over the study period. RESULTS: There were 136 208 admissions to general internal medicine involving 88 121 unique patients over the study period. General internal medicine admissions accounted for 38.8% of all admissions from the emergency department and 23.7% of all hospital bed-days. Over the study period, the number of admissions to general internal medicine increased by 32.4%; there was no meaningful change in the median length of stay or cost per hospital stay. The median patient age was 73 (interquartile range [IQR] 57-84) years, and the median number of coexisting conditions was 6 (IQR 3-9). The median acute length of stay was 4.6 (IQR 2.5-8.6) days, and the median total cost per hospital stay was $5850 (IQR $3915-$10 061). Patients received at least 1 computed tomography scan in 52.2% of admissions. The most common primary discharge diagnoses were pneumonia (5.0% of admissions), heart failure (4.7%), chronic obstructive pulmonary disease (4.1%), urinary tract infection (4.0%) and stroke (3.6%). INTERPRETATION: Patients admitted to general internal medicine services represent a large, heterogeneous, resource-intensive and growing population. Understanding and improving general internal medicine care is essential to promote a high-quality, sustainable health care system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".