Comparison of Disease Clusters in Two Elderly Populations Hospitalized in 2008 and 2010
Bibliographic record
Abstract
BACKGROUND: As chronicity represents one of the major challenges in the healthcare of aging populations, the understanding of how chronic diseases distribute and co-occur in this part of the population is needed. OBJECTIVES: The aims of this study were to evaluate and compare patterns of diseases identified with cluster analysis in two samples of hospitalized elderly. METHODS: Data were obtained from the multicenter 'Registry Politerapie SIMI (REPOSI)' that included people aged 65 or older hospitalized in internal medicine and geriatric wards in Italy during 2008 and 2010. The study sample from the first wave included 1,411 subjects enrolled in 38 hospitals wards, whereas the second wave included 1,380 subjects in 66 wards located in different regions of Italy. To analyze patterns of multimorbidity, a cluster analysis was performed including the same diseases (19 chronic conditions with a prevalence >5%) collected at hospital discharge during the two waves of the registry. RESULTS: Eight clusters of diseases were identified in the first wave of the REPOSI registry and six in the second wave. Several diseases were included in similar clusters in the two waves, such as malignancy and liver cirrhosis; anemia, gastric and intestinal diseases; diabetes and coronary heart disease; chronic obstructive pulmonary disease and prostate hypertrophy. CONCLUSION: These findings strengthened the idea of an association other than by chance of diseases in the elderly population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".