Internal medicine hospitalisations and liver disease: a comparative disease burden analysis of a multicentre cohort
Bibliographic record
Abstract
BACKGROUND: Liver disease is an increasing burden on population health globally. AIMS: To characterise burden of liver disease among general internal medicine inpatients at seven Toronto-area hospitals and compare it to other common medical conditions. METHODS: Data from April 2010 to October 2017 were obtained from hospitals participating in the GEMINI collaborative. Using these cohort data from hospital information systems linked to administrative data, we defined liver disease admissions using most responsible discharge diagnoses categorised according to international classification of diseases, 10th Revision-enhanced Canadian version (ICD-10-CA). We identified admissions for heart failure, chronic obstructive pulmonary disease (COPD) and pneumonia as comparators. We calculated standardised mortality ratios (SMRs) as the ratio of observed to expected deaths. RESULTS: Among 239 018 discharges, liver disease accounted for 1.7% of most responsible discharge diagnoses. Liver disease was associated with marked premature mortality, with SMR of 8.84 (95% CI 8.06-9.67) compared to 1.06 (95% CI 0.99-1.12) for heart failure, 1.05 (95% CI 0.96-1.15) for COPD and 1.28 (95% CI 1.20-1.37) for pneumonia. The majority of deaths were among patients younger than 65 years (57.7%) compared to 3.3% in heart failure, 5.6% in COPD and 10.7% in pneumonia. Liver disease patients presented with worse Laboratory-Based Acute Physiology Scores, were more frequently admitted to the intensive care unit (14.4%), incurred higher average total costs (median $6723 CAD), had higher in-hospital mortality (11.4%), and were more likely to be a readmission from 30 days prior (19.8%). Non-alcoholic fatty liver disease admissions increased from 120 in 2011-2012 to 215 in 2016-2017 (P < 0.01). CONCLUSION: In Canada's largest urban centre, liver disease admissions resulted in premature morbidity and mortality with higher resource use compared to common cardio-respiratory conditions. Re-evaluation of approaches to caring for inpatients with liver disease is timely and justified.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".