Enhancing Self-Management of Percutaneous Endoscopic Gastrostomy Tubes Through the Implementation of a Standardized Education and Assessment Pathway
Bibliographic record
Abstract
Intraduodenal infusion of levodopa-carbidopa intestinal gel by percutaneous endoscopic gastrostomy tube with jejunal extension is a treatment option to reduce motor and nonmotor complications in patients with advanced Parkinson's disease when oral therapy no longer provides sufficient benefit. Medication management is of central focus; however, there was no standardized patient education on stoma-site care and tube maintenance, leading to the development of stoma-site complications. As a quality improvement (QI) initiative, a standardized education and assessment pathway was developed and implemented in an urban academic outpatient clinic to enhance patient self-management and reduce stoma-site complications. A retrospective chart review was conducted to establish baseline incidence of cutaneous stoma-site complications. QI interventions were implemented using a rapid-cycle improvement model. Routine stoma assessments by a nurse who specializes in wound, ostomy, and continence care were implemented at set points, and patient education on PEG tube care and maintenance was reinforced at each session. Results demonstrated a significant reduction in moderate-to-severe tube and stoma-site-related complication. Implementation of a similar standardized education and assessment pathway in patients with percutaneous endoscopic gastrostomy tubes may lead to a decrease in stoma-site-related complications and overall better patient self-management.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".