The Effects of Glucose Therapy Agents—Apple Juice, Orange Juice, and Cola—on Enteral Tube Flow and Patency
Bibliographic record
Abstract
To develop evidence-based hypoglycemia treatment protocols in patients receiving total enteral nutrition, this study determined the effect on enteral tube flow of glucose therapy agents: apple juice, orange juice, and cola, and it also examined the effects of tube type and feed type with these glucose therapy agents. For this study, 12 gastrostomy tubes (6 polyethylene and 6 silicone) were set at 50 mL/h. Each feeding set was filled with Isosource HN with fibre or Novasource Renal. Each tube was irrigated with 1 glucose therapy agent, providing approximately 20 g of carbohydrate every 4 h. Flow-rate measurements were collected at 2 h intervals. The results showed that the glucose therapy agent choice affected flow rates: apple juice and cola had higher average flow rates than orange juice (P = 0.01). A significant difference was found between tube type and enteral formula: polyethylene tubes had higher average flow rates than silicone tubes (P < 0.0001), and Isosource HN with fibre had higher flow rates than Novasource Renal (P = 0.01). We concluded that apple juice and cola have less tube clogging potential than orange juice, and thus may be considered as primary treatment options for hypoglycemia in enterally fed patients. Polyethylene tubes and Isosource HN with fibre were less likely to clog than silicone tubes and Novasource Renal.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".