An Improvised “Blow Glove” Device Produces Similar PEP Values to a Commercial PEP Device: An Experimental Study
Bibliographic record
Abstract
BACKGROUND: Postoperative positive expiratory pressure (PEP) therapy promotes increased lung volume, secretion clearance, and improved oxygenation. Several commercial devices exist that produce recommended PEP values (10-20 cmH2O) when the patient breathes through a fixed orifice resistor. It was hypothesized that an inexpensive, improvised "blow glove" device would produce similar PEP values over a wider range of expiration volumes and flow rates. METHODS: PEP for different expiration volumes (400-2000 mL) and expiratory flow rates (10-80 L/min) was compared between a commercial PEP device (Resistex, Mercury Medical, Clearwater, FL) and an improvised "blow glove" device, recorded by a Vela ventilator (CareFusion, San Diego, CA). Dynamics in positive end expiratory pressure (PEEP) values were evaluated following five consecutive expirations. The "blow glove" device was evaluated using various glove compositions and sizes. RESULTS: The improvised "blow glove" device produced a significantly higher rate of PEP values in the recommended range than the Resistex device (88.9% vs. 20%, p<0.0001). No significant difference was observed between small and large glove sizes (88.9% vs. 82.9%, p>0.05), but the powdered latex glove showed a significantly higher rate of PEP values in the recommended range than the powder-free latex glove (88.9% vs. 44.4%, p<0.001). CONCLUSIONS: A "blow glove" PEP device using a powdered latex glove produces PEP values in the recommended range over a wider spectrum of expiratory flow rates and expiration volumes than a commercial PEP device.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".