Pediatric tracheostomy speaking valves: A multidisciplinary protocol leads to earlier initial trials
Bibliographic record
Abstract
Early speaking valve application in children with tracheostomies is encouraged for language development. Whether an institutional multidisciplinary protocol impacts the patient population and timelines for which a speaking valve is trialed has not been studied. This retrospective study compared speaking valve trials performed at a pediatric quaternary hospital over a 12-year period. Timelines (time between tracheostomy insertion, speech-language pathologist (SLP) consultation, speaking valve order, and trial) and patient characteristics (demographics, tracheostomy classification, and feeding status) were collected. Medians (IQRs) compared timelines before and after a protocol was instituted and compared the timelines between tracheostomy classifications. Median time between tracheostomy insertion and SLP consultation did not change: before protocol-1.8 (7.7) months and after protocol-1.8 (2.4) months. Time between tracheostomy insertion and speaking valve trial decreased: before protocol-34.1 (40.5) months and after protocol-12.9 (8.4) months. Time between tracheostomy insertion and trial was not different between tracheostomy classifications: upper airway obstruction-16.0 (27.1) months, complex medical condition-36.3 (45.8) months, and invasive ventilation-17.5 (22.3) months. An institutional multidisciplinary protocol decreases the time between tracheostomy insertion and speaking valve trial, regardless of the reason the tracheostomy is needed in the pediatric population.
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 | 0.132 | 0.216 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".