How intravenous nitroglycerine transit time from bag-to-bloodstream can be affected by infusion technique: a simulation study
Bibliographic record
Abstract
OBJECTIVE: To measure the possible delays in intravenous nitroglycerine administration. METHODS: This was a simulation study of sham intravenous nitroglycerine using a standard nitroglycerine titration protocol. Variables studied were (i) common cannulae/needles, (ii) infusion accessories and (iii) presence of a parallel intravenous saline carrier line (or drive line) infusing at 30 mL/h. Outcomes were (i) delay from bag-to-bloodstream arrival and (ii) the dosage showing on the infusion pump when the sham drug first exits the cannula (aka the 'presumed initial dosage'). RESULTS: There was a statistically significant difference in both (i) time-to-bloodstream arrival and (ii) the dosage showing on the infusion pump as the sham first exits the cannula with (i) different cannulae, (ii) different accessories and (iii) presence of a carrier line. The bag-to-bloodstream time varied 10-fold: 197-2062 s. The 'presumed initial dosage' varied sixfold: 5-30 µg/min. Adding the medication to an already flowing carrier line reduced the time for the sham to exit the cannula fourfold: from 2062 to 469 s. CONCLUSIONS: Despite limitations, this study outlines the importance of cannula type, infusion accessories and carrier lines. Larger cannulae and greater priming volumes substantially delay drug delivery, whereas carrier lines/drive lines substantially accelerate drug delivery. Our study also shows how patients could be exposed to clinical delays, as well as incorrect presumptions about drug dosage. Guidelines, and education efforts, should highlight the clinical importance of factors that affect bag-to-bloodstream time.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".