Repeated thermo‐sterilisation further affects the reliability of positive end‐expiratory pressure valves
Bibliographic record
Abstract
AIM: Positive end-expiratory pressure (PEEP) valves are used together with self-inflating bags (SIB) to provide a preset PEEP during manual ventilation. It has recently been shown that these valves deliver highly variable levels of PEEP. We hypothesised that material fatigue due to repeated thermo-sterilisation (TS) may contribute to varying reliability of PEEP valves. METHODS: In a laboratory study 10 new PEEP valves were tested before and after 10, 20 and 30 cycles of routine TS (7 min at 134°C) by using a neonatal lung model (compliance 0.2 mL/kPa). Settings were positive inflation pressure = 20 and 40 cm H(2)O, PEEP = 5 and 10 cm H(2)O, respiratory rate = 40 and 60/min, flow = 8l/min. PEEP was recorded using a respiratory function monitor. RESULTS: Before TS, a mean (standard deviation) PEEP of 4.0 (0.9) and 7.7 (1.0) cm H(2)O was delivered by the 10 valves when the PEEP was set to 5 and 10 cm H(2)O, respectively. One new valve only delivered 2.0 (0.0) and 5.0 (0.0) cm H(2)O when the PEEP was adjusted to 5 and 10 cm H(2)O, respectively. Four of the 10 investigated valves showed significant variations in PEEP (coefficient of variation >10%) throughout the autoclaving process. One valve completely lost its function after the 20th TS. Common defects were tears in the softer materials or displacement of the rubber seal. Six of the 10 valves continued to provide PEEP in spite of repeated TS. CONCLUSION: The reliability of PEEP valves is affected by repeated TS. Multi-use PEEP valves should be tested for reliable PEEP provision following TS.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".