Timing and Outcomes of Percutaneous Endoscopic Gastrostomy After Ischemic Stroke
Bibliographic record
Abstract
Background: Guidelines recommend using percutaneous endoscopic gastrostomy (PEG) for dysphagia after 2 weeks of stroke onset. We aimed to study the impact of PEG timing on outcomes in patients with ischemic stroke. Methods: In this retrospective study of patients with ischemic stroke and PEG between 2014 and 2019, early PEG was defined as PEG tube placed within 14 days of stroke and late PEG after 14 days. Outcomes of 30-day mortality, PEG-related complications, and functional swallow recovery were compared between early and late PEG. Logistic regression model assessed factors associated with PEG timing. Results: The median time of PEG tube placement after stroke was 10.9 days. Of the 161 included patients, 60.9% had early PEG, and its associated patient factors were nursing facility discharge (adjusted odds ratio (OR): 3.4, confidence interval (CI): 1.48 - 7.82) and infection (OR: 0.32, CI: 0.139 - 0.178). Late PEG had 3.27 times greater odds of swallowing recovery, but mortality and complications were not significantly different between early and late PEG. Conclusions: Skilled nursing facility disposition and lack of infection were predictors of early PEG, constituting the majority of PEG placed for ischemic stroke-related dysphagia. Although better odds of swallowing recovery were seen with late PEG, likely implicating better patient selection, overall, the timing of PEG tube placement did not impact short-term mortality and complications.
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".