NP-007 Recommendations for administration of immunosuppressants via enteral feeding tube according to their<i>in-vitro</i>administration
Bibliographic record
Abstract
Background and Importance Immunosuppressants (IS) are used in the treatment and prevention of graft rejection after solid organ or tissue transplantation.1 Their administration via an enteral feeding tube (EFT) is problematic regarding their narrow therapeutic index, cytotoxic, teratogenic potential, and occupational hazard. Incomplete absorption due to incorrect administration via EFT may lead to graft rejection.2 Appropriate drug forms of IS for administration via EFT are missing in our country. Aim and Objectives Despite multiple published guidelines for the administration of medicines via EFT, available drug forms differ between countries. Our aim was to create local recommendations for the safe administration of IS via EFT reflecting the available medicines in our country, while preventing EFT occlusion and preserving optimal effect. Materials and Methods A literature search was aimed to determine the site of absorption, incompatibilities, and measures to decrease the occupational hazard. The practical part consisted of dissolving tablets, capsules’ content, and their administration via EFTs of diameters 10, 8, and 6 Fr. The administration of IS was realized by the adapted protocol by White et al., 2015.3 We evaluated the rate of disintegration of tablets and tube occlusion. Results Only one brand of mycophenolate mofetil tablets and two brands of azathioprine tablets disintegrated in a syringe. All the other tablets need to be crushed. Two of the studied IS caused the occlusion of a 6 Fr EFT, no EFT of wider diameter was occluded. We summaries our recommendations in a table. Conclusion and Relevance Crushing tablets or opening capsules is often the only possibility for IS administration via EFT. In these cases, using personal protective equipment is always needed. Ciclosporin, mycophenolate mofetil, and azathioprine can be administered relatively safely. Special attention is needed when an EFT of 6 Fr is used due to its easy occlusion. References and/or Acknowledgements Our project was supported by the National Institute of Cardiovascular Diseases, EduPharm pri NÚSCH, Bausch Health, Novartis, Nutricia, Roche. 1. Hartono et al., Cold Spring Harb Perspect Med., 2013. 2. Silva et al., J Clin Pharm Ther., 2020. 3. White et al., Handbook of drug administration via enteral feeding tubes, 2015.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.017 |
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".