Frequency of post-stroke pneumonia: Systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
BackgroundPost-stroke pneumonia and other infectious complications are serious conditions whose frequency varies widely across studies.AimsWe conducted a systematic review to estimate the frequency of post-stroke pneumonia and other types of major infection.Summary of reviewMEDLINE, EMBASE, CINAHL, and PsycINFO databases were searched for prospective studies with consecutive recruitment of stroke patients. The primary outcome was post-stroke pneumonia. Secondary outcomes were any infection and urinary tract infection. Quality assessment was done using Newcastle Ottawa scale. Heterogeneity of estimates across study populations was calculated using Cochran's Q (heterogeneity χ<sup>2</sup>) and I<sup>2</sup> statistics. A total of 47 studies (139,432 patients) with 48 sample populations were eligible for inclusion. Mean age of patients was 68.3 years and their mean National Institute of Health Stroke Scale score was 8.2. The pooled frequency of post-stroke pneumonia was 12.3% (95% confidence interval [CI] 11%–13.6%; I<sup>2</sup> = 98%). The pooled frequency from 2011 to 2017 was 13.5% (95% CI 11.8%–15.3%; I<sup>2</sup> = 98%) and comparable with earlier periods (P interaction = 0.31). The pooled frequency in studies in stroke units was 8% (95% CI 7.1%–9%; I<sup>2</sup> = 78%) and significantly lower than other locations (P interaction = 0.001). The pooled frequency of post-stroke infection was 21% (95% CI 13%–29.3%; I<sup>2</sup> = 99%) and of post-stroke urinary tract infection was 7.9% (95% CI 6.7%–9.3%; I<sup>2</sup> = 96%).ConclusionApproximately 1 in 10 stroke patients experience pneumonia during the acute period of hospital care. The frequency of post-stroke pneumonia has remained stable in recent decades but is lower in patients receiving stroke unit care compared to management in other ward settings.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 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 teacher head, 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".