Systematic Review and Meta-Analysis of Dysphagia and Associated Pneumonia in Patients With Stroke From India: A Call to Arms
Bibliographic record
Abstract
Abstract Background India has high incidence (116-163 per 100,000) of stroke compared to western countries. Stroke is reported to be the fourth leading cause of death and fifth leading cause of disability in India. Dysphagia is seen approximately in half of the stroke patients and if unidentified may result in pulmonary complications such as aspiration pneumonia. However, there is no estimate of post stroke dysphagia and associated pneumonia in India. Method Using the PRISMA methodology, a systematic search for recent literature on dysphagia in stroke was carried out across all the major databases. Two authors independently screened the titles and the abstracts, and those selected articles were assessed for quality using the GRADE approach. Comparisons were made of reported dysphagia and pneumonia frequencies, the relative risks of developing pneumonia were calculated. Data on duration of hospital stay and rates of mortality were also extracted. Results We identified 86 citations, out of which only four articles were deemed eligible for critical analyses and data extraction. A high incidence (11.1% - 87.5%) of dysphagia was observed among stroke patients. Only two studies reported on incidence of pneumonia (22.8% - 32%); only one study stratified patients by both dysphagia and pneumonia. A relative risk of 5.82 (95% CI 4.6, 7.2) was found for pneumonia in patients with stroke and dysphagia. Data on length of hospital stay and rates of mortality secondary to aspiration pneumonia are also presented. Conclusion Despite high incidence of dysphagia and associated pneumonia the methodological quality of studies are low. There is a dire need for methodologically sound studies to accurate determine the incidence of dysphagia and its impact on stroke patients in India.
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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 | 0.045 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".