Physicochemical Compatibility of Dexmedetomidine With Parenteral Nutrition
Bibliographic record
Abstract
BACKGROUND: Dexmedetomidine is an α2-agonist used as a sedative agent in the intensive care setting. Simultaneous administration of dexmedetomidine and parenteral nutrition (PN) may be required. The aim of this study was to evaluate the physicochemical compatibility of dexmedetomidine Y-site administered with PN. METHODS: Three PN and 3 dexmedetomidine solutions were compounded. The tested infusion rate for PN was 66 mL/h. For dexmedetomidine, we considered the initial and maximum infusion rates (0.7 and 1.4 µg/kg/h) detailed in the data sheet. Taking this into account and considering a weight range of 55-95 kg, we tested 2 dexmedetomidine infusion rates (10 and 36 mL/h). The samples obtained were examined visually against light. pH was analyzed with a pH meter. Mean fat droplet diameter was determined by dynamic light scattering. Quantification of dexmedetomidine concentration was carried out by ultraperformance liquid chromatography-high-resolution mass spectrometry. For each PN-dexmedetomidine admixture, tests were performed in triplicate. RESULTS: No alterations were observed by visual inspection. Average pH was 6.25 ± 0.01. Droplet diameter remained below 500 nm (298 ± 10 nm for 10-mL/h rate and 303 ± 5 nm for 36-mL/h rate). Dexmedetomidine concentrations at t = 0 were 519 ± 31 ng/mL and 1391 ± 90 ng/mL for 10- and 36-mL/h infusion rates, respectively. At t = 24 hours, the concentrations obtained were 494 ± 22 and 1332 ± 102 ng/mL, which translates into ≥90% of the initial concentrations. CONCLUSION: Dexmedetomidine is physicochemically compatible with PN during simulated Y-site administration at the tested infusion rates.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".