A Protocol for the Prospective Evaluation of Novel Suction-Based Airway Clearance Devices in the Treatment of Foreign Body Airway Obstructions
Bibliographic record
Abstract
Background Foreign body airway obstructions (FBAOs, choking) are a significant cause of preventable mortality. Abdominal thrusts, back blows, and chest compressions are traditional interventions. However, suction-based airway clearance devices (ACDs) have recently been marketed as an alternative. Of note, there is limited published evidence regarding their efficacy and safety. Our research has two aims: (1) to investigate what situational and patient factors are frequently identified, and which are associated with relief of the FBAO and survival in individuals with FBAOs treated with an ACD; and (2) to describe the experience of individuals who have used ACDs in response to a FBAO and identify facilitators and barriers to the use of ACDs compared to traditional interventions. Methods and analysis A prospective database will be developed using an online reporting system to capture ACD uses, independent of manufacturers, from July 1st, 2021 to December 31st, 2023. Descriptive statistics will be used to summarize cases, outcomes, and adverse events. Clinically important subgroups will be stratified for analysis, including the severity of obstruction, patient demographics, and training of ACD users. Semi-structured interviews will also be conducted with a subset of ACD users to describe in detail their experience using the device. Themes from these interviews will be assessed using the theoretical domains framework. Discussion This study will improve the evidence surrounding ACDs and compare it to current data for traditional techniques, with the aim of optimizing FBAO treatment. Data on ACDs are urgently needed as these devices are already being used by parents, caregivers, lay rescuers, and healthcare professionals to respond to choking emergencies. This evaluation will provide important information about their effectiveness and any safety concerns which can inform the public, resuscitation guidelines, and future research studies.
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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 | 0.088 | 0.085 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.139 | 0.038 |
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".