P.059 Predictors of gastrostomy tube placement in patients with dysphagia after acute stroke
Bibliographic record
Abstract
Background: In patients with acute stroke, nasogastric (NG) tubes are commonly inserted for feeding when dysphagia is identified, and percutaneous endoscopic gastrostomy (PEG) tubes are placed for severe or persistent dysphagia. However, little is known regarding predictors of PEG insertion. Methods: We used the Ontario stroke registry from 2003-2013 to identify baseline characteristics of all patients with NG or PEG tube insertion after stroke. We used multiple logistic regression with backwards selection to determine variables that were independent predictors of PEG tube insertion during admission. Results: 4002 patients with NG and 1903 patients with PEG were included in the analysis. Independent predictors of PEG were: Age (80+ vs. <60; odds ratio [OR] 1.70), past history of stroke (OR 1.17), higher stroke severity (severe vs. mild stroke; OR 1.37), stroke unit admission (OR 1.46), and dysphagia screening (OR 1.52). Factors associated with reduced odds of PEG insertion were: Prior history of peptic ulcer disease (OR 0.70), prior independence (OR 0.78), dementia (OR 0.76), palliative status (OR 0.49), and thrombolysis (OR 0.66). *All p<0.01 Conclusions: The strongest predictors of PEG were older age, higher stroke severity, stroke unit admission and dysphagia screening. Patients with dementia had reduced odds of PEG. Thrombolysis also reduced odds of PEG and may be protective.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.008 | 0.001 |
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".