Costs of Care for Neonates with Hypoxic-Ischemic Encephalopathy Treated with Therapeutic Hypothermia and Validation of the Canadian Neonatal Network Costing Algorithm
Bibliographic record
Abstract
Objective: Therapeutic hypothermia (TH) is the standard treatment for neonates with hypoxic-ischemic encephalopathy (HIE). Validated cost estimates are required to better evaluate the cost-effectiveness of additional interventions during TH. The goal of this study is to identify clinical factors associated with costs of care and validate the Canadian Neonatal Network (CNN) costing algorithm for neonates with HIE receiving TH. Study design: Single-center retrospective cohort study among neonates with HIE treated with TH in a tertiary neonatal intensive care unit from 2016 to 2018. Actual costs per patient were obtained from the hospital cost accounting system, Coût par Parcours de Soinset de Services, and linked to patient data. Estimated costs per patient were calculated using the CNN case-costing algorithm. Neonates were grouped into cost tertiles to identify characteristics of high resource users. Comparisons of actual costs and estimated costs were performed across 8 cost domains. Results: < .01). The mean difference in total costs between estimates was $5339 (95% CI: $2697, $7981). There was a moderate-to-strong correlation between actual and estimated costs in 5/8 cost domains (R range: 0.68-0.98). Conclusions: Severity of HIE and other markers of disease severity were associated with higher hospital costs. The CNN costing algorithm cost estimates for neonates with HIE treated with TH highly correlate with actual costs but overestimates the costs by approximately 15%.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".