Long length of hospital stay in children with medical complexity
Bibliographic record
Abstract
BACKGROUND: Hospitalizations of children with medical complexity (CMC) account for one-half of hospital days in children, with lengths of stays (LOS) that are typically longer than those for children without medical complexity. The objective was to assess the impact of, risk factors for, and variation across children's hospitals regarding long LOS (≥10 days) hospitalizations in CMC. METHODS: A retrospective study of 954,018 CMC hospitalizations, excluding admissions for neonatal and cancer care, during 2013 to 2014 in 44 children's hospitals. CMC were identified using 3M's Clinical Risk Group categories 6, 7, and 9, representing children with multiple and/or catastrophic chronic conditions. Multivariable regression was used to identify demographic and clinical characteristics associated with LOS ≥10 days. Hospital-level risk-adjusted rates of long LOS generated from these models were compared using a covariance test of the hospitals' random effect. RESULTS: Among CMC, LOS ≥10 days accounted for 14.9% (n = 142,082) of all admissions and 61.8% ($13.7 billion) of hospital costs. The characteristics most strongly associated with LOS ≥10 days were use of intensive care unit (ICU) (odds ratio [OR]: 3.5, 95% confidence interval [CI]: 3.4-3.5), respiratory complex chronic condition (OR: 2.7, 95% CI: 2.6-2.7), and transfer from another medical facility (OR: 2.1, 95% CI: 2.0-2.1). After adjusting for severity, there was significant (P < 0.001) variation in the prevalence of LOS ≥10 days for CMC across children's hospitals (range, 10.3%-21.8%). CONCLUSIONS: Long hospitalizations for CMC are costly. Their prevalence varies significantly by type of chronic condition and across children's hospitals. Efforts to reduce hospital costs in CMC might benefit from a focus on prolonged LOS. Journal of Hospital Medicine 2016;11:750-756. © 2016 Society of Hospital Medicine.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".