Prognostic factors associated with mortality and major in-hospital complications in patients with bacteremic pneumococcal pneumonia
Bibliographic record
Abstract
Bacteremic pneumococcal pneumonia (BPP) causes considerable mortality and morbidity. We aimed to identify prognostic factors associated with mortality and major in-hospital complications in BPP.A prospective, population-based clinical registry of 1636 hospitalized adult patients (≥18 years) with BPP was established between 2000 and 2010 in Northern Alberta, Canada. Prognostic factors for mortality and major in-hospital complications (e.g., cardiac events, mechanical ventilation, aspiration) were evaluated using multivariable logistic regression.Average age was 54 (standard deviation 18) years, 57% males, and 59% had high case-fatality rate (CFR) serotypes. Overall, 14% (226/1636) of patients died and 22% (315/1410) of survivors developed at least 1 complication. Independent prognostic factors for mortality were age (adjusted odds ratio [aOR], 1.5 per decade; 95% confidence interval [CI], 1.3-1.7), nursing home residence (aOR, 3.7; 95% CI 1.8-7.4), community-dwelling dementia (aOR 3.7; 95% CI, 1.6-8.6), alcohol abuse (aOR, 2.2; 95% CI, 1.4-3.4), acid-suppressing drugs (aOR, 1.5; 95% CI, 1.0-2.3), guideline-discordant antibiotics (aOR, 3.4; 95% CI, 2.4-4.8), multilobe pneumonia (aOR, 2.6; 95% CI, 1.8-3.6), and high CFR serotypes (aOR, 1.8; 95% CI, 1.2-2.8). Similar prognostic factors were observed for major in-hospital complications. Pneumococcal vaccination was associated with reduced in-hospital mortality (aOR, 0.2; 95% CI, 0.05-0.9) but not major complications (P = 0.2).Older and frailer patients, and those who abuse alcohol or take acid-suppressing drugs, are at increased risk of BPP-related mortality and complications, as are those with high CFR serotypes. Beyond identifying those at highest risk, our findings demonstrate the importance of guideline-concordant antibiotics and pneumococcal vaccination in those with BPP.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".