Pneumatic tube validation: Reducing the need for donor samples by integrating a vial-embedded data logger
Bibliographic record
Abstract
BACKGROUND: The most common way to validate a pneumatic tube system is to compare pneumatic tube system-transported blood samples to blood samples carried by hand. The importance of measuring the forces inside the pneumatic tube system has also been emphasized. The aim of this study was to define a validation protocol using a mini data logger (VitalVial, Motryx Inc., Canada) to reduce the need for donor samples in pneumatic tube system validation. METHODS: As an indicator of the total vibration, the blood samples are exposed to under pneumatic tube system transportation; the area under the curve was determined by a VitalVial for all hospital Tempus600 lines using a five-day validation protocol. Only the three lines with the highest area under the curves were clinically validated by analysing potassium, lactate dehydrogenase and aspartate aminotransferase. A month after pneumatic tube system commissioning, a follow-up on laboratory data was performed. RESULTS: Mean area under the curve of the six lines ranged between 347 and 581. The variability of the area under the curve was between 1.51 and 11.55%. In the laboratory data follow-up, an increase in lactate dehydrogenase haemolysis was seen from the three lines with the highest area under the curve and the emergency department, which was not detected in the clinical validation. When the Tempus600 system was in commission, a higher mean area under the curve was measured. CONCLUSION: A three-day validation protocol using VitalVials is enough to determine the stability of a Tempus600 system and can greatly reduce the need for donor samples. When in commission, the stability of the pneumatic tube system should be verified and lactate dehydrogenase haemolysis should be routinely checked.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".