Predictors of Pediatric Tracheostomy Outcomes in the United States
Bibliographic record
Abstract
OBJECTIVES: To investigate the outcomes of pediatric tracheostomy as influenced by demographics and comorbidities. STUDY DESIGN: Retrospective national database review. SETTING: Fifty-two children's hospitals across the United States. SUBJECTS AND METHODS: Hospitalization records from Pediatric Health Information System database dated 2010 to 2018 with patients younger than 18 years and procedure codes for tracheostomy were extracted. The primary outcome was total length of stay. The secondary outcomes were 30-day readmission, mortality, and posttracheostomy length of stay. RESULTS: < .001). On multivariate regression analyses, the total and posttracheostomy lengths of stay were significantly increased in children younger than 1 year, patients of black race, hospitals in the non-West regions, those discharged to home, and those with comorbidities. Socioeconomic indicators such as insurance type and estimated household income were associated with no difference or small effect sizes. Regions and comorbidities were associated with differences in 30-day readmission (overall 26%), while in-hospital mortality was primarily associated with age and comorbidities (overall 8.6%). CONCLUSION: Pediatric tracheostomy requires substantial health care resources with length of stay escalating over recent years. Age, race, region, discharge destination, and comorbidities were associated with differences in length of stay.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".