Long-Term Survival after Hospitalization for Community-Acquired and Healthcare-Associated Pneumonia
Bibliographic record
Abstract
BACKGROUND: Elderly patients surviving community-acquired pneumonia (CAP) have subsequent increased mortality. However, little is known regarding long-term survival in younger adults or those with healthcare-associated pneumonia (HCAP). OBJECTIVES: To identify factors associated with mortality and compare long-term survival in patients hospitalized with HCAP to that of patients with CAP. METHODS: We determined survival after discharge as of December 2002 in a patient cohort admitted with pneumonia between June 1994 and May 1996. We used the Cox proportional hazard model to estimate differences in survival after controlling for confounders. RESULTS: Of the 522 patients hospitalized with pneumonia, 457 survived to discharge. One hundred sixty-four patients (36%, 95% confidence interval, CI, 31-40%) were admitted with HCAP, while 293 (64%, 95% CI 60-69%) were admitted with CAP. Of the 181 deaths in the follow-up period, 70 occurred in patients under age 65 years admitted with HCAP (53% death rate, 95% CI 44-62%). Nineteen of these deaths (27%, 95% CI 17-39%) occurred in the absence of HIV infection. In patients under the age of 65 whose only risk factor for HCAP was treatment for pneumonia or hospitalization in the previous 90 days, 4 of 13 patients (31%, 95% CI 9-61%) died. Twenty percent (95% CI 15-26%) of patients under age 65 years admitted with CAP died during the follow-up. CONCLUSIONS: Admission for HCAP, and to a lesser degree CAP, is associated with increased long-term mortality even in young patients. Future studies are warranted to identify interventions to improve survival 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".