How important is age in defining the prognosis of patients with community-acquired pneumonia?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Given that the population is increasing in age, a better understanding of the relationship between chronological age and health-related outcomes (especially mortality) is needed, for both chronic diseases (e.g. diabetes) and acute illnesses (e.g. pneumonia). Our purpose was to review the impact of age on the prognosis of patients with community-acquired pneumonia (CAP). RECENT FINDINGS: Many studies in patients with CAP have suggested that chronological age is not necessarily independently associated with mortality. Poorer outcomes in the elderly with CAP have been related to severity of disease, comorbid disease burden, functional status, and frailty, but not to age alone. However, many of these studies suffer from 'over-adjustment' due to the use of unmodified severity scores such as the Pneumonia Severity Index or Acute Physiology and Chronic Health Evaluation II (that already include age) in multivariable analyses. Studies accounting for this over-adjustment suggest that age is, in fact, independently associated with mortality in hospitalized patients with CAP. Other outcomes including hospitalization and readmission rates, hospital length of stay, and cost of care are similarly associated with increasing age. Residual confounding is still a problem in many of the observational studies reviewed. SUMMARY: Contrary to conventional wisdom, chronological age is independently associated with adverse outcomes in patients with CAP. Until better methods (or more clinically-rich datasets) for observational studies are developed that can avoid over-adjustment and better deal with residual confounding, physicians should take into account both a patient's overall health status and his or her chronological age.
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".