Comparison of hospital course of geriatric inpatients with or without active cancer: a bicentric case-control study
Bibliographic record
Abstract
BACKGROUND: The hospital course of older patients with cancer hospitalized in geriatrics units remain poorly known. The aim of our study was to compare the clinical characteristics and hospital courses of geriatric inpatients with or without active cancer. METHODS: A case-control study was conducted in 2013 in the geriatric acute care units of Angers University Hospital and Le Mans Hospital, France, to compare 204 consecutive cases with cancer (mean age, 85.4±5.5 years; 42.6% male) and 1,020 controls without active cancer (mean age, 85.5±5.8 years; 42.6% male) matched for age, gender, recruitment period and center. Hospital courses were evaluated by the length of hospital stay and the in-hospital mortality. The place of life, body mass index, cumulative illness rating scale-geriatrics (CIRS-G) score, history of falls, and reason for admission were used as covariates. RESULTS: Cases with active cancer exhibited a higher (i.e., worse) CIRS-G score (p<0.001) and were hospitalized more often for an organic failure (p<0.001) than controls. The hospital stay of cases was longer (16.3±13.0 days versus 12.6±9.4 days, p<0.001), and their in-hospital mortality rate was higher than controls (23.5% versus 5.6%, p<0.001). After adjustment, having an active cancer was associated with increased length of hospital stay (β=3.3, p<0.001) and greater in-hospital mortality (OR=4.4, p<0.001). CONCLUSION: The length of hospital stay and in-hospital mortality rate were greater in geriatric patients with active cancer compared to controls, which reflects more complicated hospital courses in this 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".