Developing a special face mask with an angle meter to optimize the head position while performing bag–valve–mask ventilation—a prospective simulated proof-of-concept study
Bibliographic record
Abstract
Abstract Background In unconscious patients in the supine position, the loss of soft tissue tension results in obstruction of the upper airway. Unexperienced rescuers may be unable to perform efficient bag–valve–mask ventilation due to difficulties in detecting the optimal head position to open the airway. If the ventilation mask were to indicate an optimized head position to the rescuer, bag–valve–mask ventilation could possibly be optimized. Methods A digital sensor was attached to a face mask to measure the degree of head reclination. We attached this face mask to an airway trainer and sealed the mask to its face with tape; the airway trainer was connected to a test lung and ventilated in a pressure-controlled mode by a standard anesthesia machine (Pmax 10 mbar, PEEP 0 mbar, F 12/min). Its head was extended starting from the neutral position to 42 degrees in steps of 2 degrees. The primary endpoints were the correlation of preset angles and our face mask’s digitally measured head position angles. We further evaluated the tidal and minute ventilation volume depending on head reclination. Results The preset head position angles correlated significantly (R2 = 0.9895855684; P<.001) with the digitally measured head position angles. In head position angles <10 degrees, the tidal volume was 150 mL; at 18 degrees, it was 200 mL; at 25 degrees, it was 450 mL; and it levelled off at 30 degrees with about 500 mL. Conclusion Digital head position angle measurement correctly detected the head position in this study. A signal in a face mask could be a helpful tool to indicate to first responders or relatively inexperienced rescuers the optimized head position during emergency ventilation.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| 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".