Tracheostomy Tube Monitoring Accessory to Detect Accidental Decannulation and Obstruction Emergencies in Ventilator-Independent Pediatric Patients
Bibliographic record
Abstract
Introduction: Some of the leading causes of tracheostomy-related complications in pediatric populations are accidental decannulation and obstruction events that result in permanent neurological damage or death if not mitigated quickly enough. Despite the severity of tracheostomy tube emergency events, there is currently no effective medical technology available that can specifically detect accidental decannulation or obstruction in a tracheostomized patient that is not ventilator dependent. Therefore, a pediatric tracheal breathing model system was developed to assist with testing new tracheostomy tube technologies for identifying emergency events. Methods: A custom carbon dioxide monitoring tracheostomy tube attachment was engineered to collect breathing waveform data during emergency events (e.g., improper insertion, accidental decannulation, and mucus obstruction). Anatomically accurate pediatric tracheal models for various age groups (0-3 months, 2-4 years, and 10-12 years old) were developed with modelling software and a 3D printer. A breathing simulator was integrated with the tracheal models to generate age-dependent respiration patterns during simulated tracheostomy tube emergencies. Results: Carbon dioxide readings from the custom tracheostomy tube attachment indicated distinct waveform recordings during simulated tracheostomy tube emergency events for all age groups tested. During incorrect insertion, accidental decannulation, and complete blockage of a tracheostomy tube, exhaled carbon dioxide readings remained static at ambient levels. Partial mucus obstruction of a tracheostomy tube decreased exhaled carbon dioxide waveform amplitude relative to unobstructed conditions. Conclusions: The tracheostomy tube attachment successfully recorded respiration patterns during simulated tracheostomy tube emergencies in pediatric patients of varying age. Breathing waveform data collected from the model system will aid in the development of emergency airway event detection software integrated in the tracheostomy tube sensing accessory.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".