Enteral Feeding Using Levodopa‐Carbidopa Intestinal Gel Percutaneous Endoscopic Gastrostomy Tube
Bibliographic record
Abstract
In Parkinson's disease (PD), dysphagia has a significant influence on nutritional status. 1 When dysphagia is resistant to oral nutrition intervention, enteral nutrition using a percutaneous endoscopic gastrostomy (PEG) can be considered.2 A levodopa (L-DOPA)-carbidopa intestinal gel (LCIG) has been used to treat advanced PD using a PEG with a jejunal extension (PEG-J) (AbbVie, Montr eal, Canada).We present a case in which the LCIG PEG-J was used successfully for feeding. Case DescriptionA 71-year-old man treated with LCIG was referred because he suffered from recurring aspiration pneumonia.A swallowing evaluation showed moderate oro-pharyngeal dysphagia.Despite modified food textures, food enrichment, supplementation, and appropriate nutrition counseling, the patient experienced moderate-to-severe malnutrition.Enteral nutrition was initiated using a 15-Fr (5-mm) LCIG PEG-J through the gastric port.Nutrition was first administered with a feeding pump during the night while infusion of LCIG was stopped.To complement his nutritional intake, he received two boluses during the day.After a few weeks, as a result of sleeping discomfort, a new isocaloric enteral nutrition plan was established.Five boluses were administered with the feeding pump simultaneously with LCIG to mimic usual meal times. FIGURE 1An adaptor had to be crafted to ensure compatibility between the gastric port of the LCIG PEG-J tube and the enteral nutrition tube.A: Standard PEG connection.B: LCIG PEG-J adapted for enteral nutrition (20 Fr PEG-J is required to allow for administration of medication).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".