Pneumonia and Lower Respiratory Infections in Nursing Home Residents: Predictors of Hospitalization and Mortality
Bibliographic record
Abstract
OBJECTIVES: To compare predictors of hospitalization and death in nursing home residents with pneumonia and other lower respiratory infections (LRIs). DESIGN: A nested cohort study. SETTING: Nine nursing homes in southern Ontario. PARTICIPANTS: Three hundred fifty-three nursing home residents with LRIs (enrolled in the control arm of a clinical trial). MEASUREMENTS: Comorbidities, vaccination status, age, health-related quality of life, functional status, and vital statistics were evaluated as potential predictors of hospitalization and mortality at 30 days. RESULTS: Moderate to high disease severity score on a practical severity scale was a strong independent predictor of hospitalization (odds ratio (OR)=7.12, P<.001) and mortality (OR=5.04, P=.003). Diagnosis of pneumonia, established using chest radiograph, was also associated with hospitalization (OR=2.43, P=.008) and mortality (OR=2.35, P=.02). Oxygen saturation (<90%) was a strong independent predictor of hospitalization (OR=3.02, P=.004) but was not a significant predictor of mortality in multivariable analyses. Diagnosis of congestive heart failure (OR=2.26, P=.02) was an independent predictor of hospitalization, whereas receipt of pneumococcal vaccine (OR=0.36, P=.01) and greater functional independence (OR=0.92, P=.02) were negatively associated with hospitalization. CONCLUSION: In nursing home residents with LRI, severity of illness and radiographically confirmed pneumonia are predictive of death and hospitalization.
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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 | 0.000 |
| 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) | 0.001 | 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".