Use of a classification of indications for pediatric tracheostomy in quality improvement (QI)
Bibliographic record
Abstract
Background: Caring for children with a tracheostomy at home may reduce costs. Prior to tracheostomy, in our institution, children may be cared for in inpatient (IP) wards or PICU. After tracheostomy, initial care occurs in PICU, then in a special section of IP area before transfer home. We initiated QI to improve care and shorten length of stay. We noted current classifications of indications for tracheostomy are based on anatomic/pathological findings, of limited value in QI. Objective: To review indications for tracheostomy and develop a classification system To map a child’s progress using this classification To use this data to identify opportunities for QI Methods: Tracheostomies were created in 100 children from 2005 to 2017, and we summarised the indications thus: Isolated airway anomaly (e.g. subglottic stenosis ), n=51 Complex syndrome with airway anomalies (e.g. Treacher Collins Syndrome) n=21 Tracheostomy to allow invasive ventilation (e.g. neuromuscular disease). N= 28 Results: Time (days) in different parts of our institution Conclusion: Categorization of indication for tracheostomy proved useful in QI. This categorization can be used by others to address service needs and compare programs
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".