An evaluation of hard-shell venous reservoir integrated pressure relief valve pressure mitigation performance
Bibliographic record
Abstract
INTRODUCTION: Vacuum assisted venous drainage (VAVD) requires the sealing of the hard-shell venous reservoir, thereby creating circumstances where reservoir pressurization may occur. Manufacturers utilize integrated pressure relief valves (IPRV) to mitigate pressurization risk; however, accidents have been reported even with these devices. We have undertaken a performance evaluation of IPRV's in a large number of hard-shell venous reservoirs. METHODS: Reservoirs were sealed and gas insufflated while measuring reservoir internal pressure. Linear regression models were developed to depict the association between internal pressure and gas inflow rate. External secondary one-way valves (ESOV) were assessed for pressure mitigation performance. An assisted venous drainage survey was circulated to Canadian Clinical Perfusionists. RESULTS: < 0.001) in internal reservoir pressures (range: 0.04-161.41 mmHg) was observed across the titrated gas inflow rate (0.5-10.0 l/min). The regression models demonstrate excellent predictive performance (SE: 0.008-0.309). ESOV's reduce the reservoir pressure below that of the IPRV; however, they cannot eliminate reservoir pressurization. The survey showed a majority (91%) of respondents use VAVD, and reservoir pressurization events occur regularly (18%). CONCLUSIONS: Significant variability among reservoir's IPRV to mitigate reservoir pressurization exists. The predictive models are extremely accurate at estimating the internal pressure. ESOV performance limitations moderate their utility as a backup pressure mitigation technique. A significant number of reservoir pressurization events are occurring with the use of VAVD. As a result, standardized communication from manufacturers on the purpose and performance of IPRV is recommended in order to delineate the limitations of these devices.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".