Increased Long-Term Mortality after an Episode of Community-Acquired Pneumonia--Time to Move beyond Descriptive Studies
Bibliographic record
Abstract
In this issue of Clinical Infectious Diseases, Mortensen et al. [1] report the results of an analysis of factors associated with long-term mortality in patients who survived 90 days after presentation to the hospital with community-acquired pneumonia (CAP) [1]. The population studied was the cohort of patients enrolled in the Boston and Pittsburgh portion of the Patient Outcomes Research Team pneumonia project, which is a collaborative effort among investigators in Boston, Pittsburgh, and Halifax, Nova Scotia, Canada [2]. Of the 1419 patients who were followed for a mean duration of 5.9 years, 608 (42.4%) died. A case-control method was used to determine whether there was increased long-term mortality among those who survived an episode of pneumonia. Control subjects were age-matched persons for whom data was obtained from US life tables. There was a significantly higher mortality among patients with CAP across all age groups than in the control population. In addition, the investigators analyzed the data to determine the factors that predicted long-term mortality. Sociodemographic factors associated with mortality were age (stratified by decade), high school graduation level or less, male sex, and nursing home residence. In addition, comorbid conditions (as represented by the Charlson comorbidity score), pleural effusion, and steroid use were independently associated with long-term mortality. Do-not-resuscitate status at the time of presentation, poor nutritional status, and glucocorticoid use were also associated with increased mortality. Fever at the time of presentation was associated with decreased mortality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".