A 2-year prospective evaluation of airway clearance devices in foreign body airway obstructions
Bibliographic record
Abstract
Aim: To collect, analyze and report the first prospective, industry-independent, data on airway clearance devices as novel foreign body airway obstruction interventions. Methods: We recruited adult airway clearance device users between July 1, 2021 and June 30, 2023 using a centralized website and email follow-up. The data collection tool captured patient, responder, situation, and outcome variables. Multi-step respondent validation occurred using electronic and geolocation verification, a random selection follow-up process, and physician review of all submitted cases. Results: We recruited 186 airway clearance device users (LifeVac©:157 [84.4%]; Dechoker©:29 [15.6%]). LifeVac© was the last intervention before foreign body airway obstruction relief in 151 of 157 cases. Of these, 150 survived to discharge. A basic life support intervention was used before LifeVac© in 119 cases, including the 6 cases where LifeVac© also failed. We identified two adverse events using LifeVac© (perioral bruising), while we could not ascertain whether another 7 were due to the foreign body or LifeVac© (3 = airway edema; 3 = oropharyngeal abrasions; 1 = esophageal perforation). Dechoker© was the last intervention before obstruction relief in 27 of 29 cases and all cases survived. A basic life support intervention was used before Dechoker© in 21 cases, including both where Dechoker© also failed. We identified one adverse event using Dechoker© (oropharyngeal abrasions). Conclusion: Within these cases, airway clearance devices appear to be effective at relieving foreign body airway obstructions. However, this data should be considered preliminary and hypothesis generating due to several limitations. We urge the resuscitation community to proactively evaluate airway clearance devices to ensure the public remains updated with best practices.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".