Predictors of Percutaneous Endoscopic Gastrostomy Tube Placement after Stroke
Bibliographic record
Abstract
AIMS: the goal of this study was to identify important prognostic variables affecting placement of a percutaneous endoscopic gastrostomy (Peg) tube after acute stroke. METHODS: We retrospectively reviewed our patient database to identify acute ischemic stroke patients who placed Peg or nasogastric tube (NGT) tube, but were free of other confounding conditions affecting swallowing. A total of 340 patients were involved in our study. We assessed the influence of age, National Institutes of Health stroke scale (NIHSS) score, infarct volume, stroke subtype based on the toAst criteria, swallowing disorders, bilateral lesions in cerebrum and length of stay (los) in a logistic regression analysis. RESULTS: In univariate analysis, age (p=0.048), NIHSS score (p<0.0001), lesion volume (p<0.0001), los (p<0.0001), stroke location (p=0.045), and swallowing disorders (p<0.0001) were found to be the primary predictors of placing Peg. the presence of lesions in bilateral cerebral was included in the final model based on clinical considerations. After multivariate adjustment, only NIHSS score (odds ratio [oR], 4.055; 95% confidence interval [CI], 2.398-6.857; p=0.0001), lesion volume (oR, 1.69; 95% CI, 1.09–4.39; p=0.014), swallowing disorders (oR, 1.151; 95% CI, 1.02-1.294; p=0.047), los (oR, 0.955; 95% CI, 0.914-0.998; p=0.0415) and bilateral lesions (oR, 2.8; 95% CI, 1.666-4.705; p=0.0001) remained significant. CONCLUSION: our data shows that NIHSS score, lesion volume, swallowing disorders, los and bilateral lesions in cerebrum can predict the requiring of Peg tube insertion in patients after stroke. Facteurs de prédiction de la mise en place d'un tube de gastrostomie par endoscopie percutanée après un accident vasculaire cérébral.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".