Decannulation following tracheostomy in children: A systematic review of decannulation protocols
Bibliographic record
Abstract
OBJECTIVE: To provide a systematic review of the existing pediatric decannulation protocols, including the role of polysomnography, and their clinical outcomes. METHODS: Five online databases were searched from database inception to May 29, 2020. Study inclusion was limited to publications that evaluated tracheostomy decannulation in children 18 years of age and younger. Data extracted included patient demographics and primary indication for tracheostomy. Methods used to assess readiness for decannulation were noted including the use of bronchoscopy, tracheostomy tube modifications, and gas exchange measurements. After decannulation, details regarding mode of ventilation, location, and length of observation period, and clinical outcomes were also collected. Descriptive statistical analyses were performed. RESULTS: A total of 24 studies including 1395 children were reviewed. Tracheostomy indications included upper airway obstruction at a well-defined anatomic site (35%), upper airway obstruction not at a well-defined site (12%) and need for long-term ventilation and pulmonary care (53%). Bronchoscopy was routinely used in 23 of 24 (96%) protocols. Tracheostomy tube modifications in the protocols included capping (n = 20, 83%), downsizing (n = 14, 58%), and fenestrations (n = 2, 8%). Measurements of gas exchange included polysomnography (n = 13/18, 72%), oximetry (n = 10/18, 56%), blood gases (n = 3,17%), and capnography (n = 3, 17%). After decannulation, children in 92% of protocols were transitioned to room air. Observation period of 48 h or less was used in 76% of children. CONCLUSIONS: There exists large variability in pediatric decannulation protocols. Polysomnography plays an integral role in assessing most children for tracheostomy removal. Evidence-based guidelines to standardize pediatric tracheostomy care remain an urgent priority.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".