The price of neonatal intensive care outcomes – in-hospital costs of morbidities related to preterm birth
Bibliographic record
Abstract
Background: Neonatal care for preterm babies is prolonged and expensive. Our aim was to analyze and report costs associated with common preterm diagnoses during NICU stay. Methods: We analyzed data from the Ontario healthcare data service. Diagnoses were collated by discharge ICD codes, and categorized by gestational age. We calculated typical non parametric statistics, and for each diagnosis we calculated median shifts and generalized linear mode. Results: We included data on 12,660 infants between 23 and 30 weeks gestation in 2005-2017. Calculated cost increment with diagnosis were: Intestinal obstruction: $94,738.08 (95%CI: $70,093.3, $117,294.2), Ventriculoperitoneal shunt: $86,456.60 (95%CI: $60,773.7, $111,552.2), Chronic Lung Disease $77,497.70 (95%CI: $74,937.2, $80,012.8), Intestinal perforation $57,997.15 (95%CI:$45,324.7, $70,652.6), Retinopathy of Prematurity: $55,761.80 (95%CI: $53,916.2, $57,620.1), Patent Ductus Arteriosus $53,453.70 (95%CI: $51,206.9, $55692.7, Post-haemorrhagic ventriculomegaly $41,822.50 (95%CI: $34,590.4, $48,872.4), Necrotizing Enterocolitis $39,785 (95%CI: $35,728.9, $43,879), Meningitis $38,871.85 (95%CI: $25,272.7, $52,224.4), Late onset sepsis $32,954.20 (95%CI: $30,403.7, 35.515), Feeding difficulties $24,820.90 (95%CI: $22,553.3, $27,064.7), Pneumonia $23,781.70 (95%CI: $18,623.8, $28,881.6), Grade >2 Intraventricular Haemorrhage $14,777.38 (95%CI: $9,821.7, $20,085.2). Adjusted generalized linear model of diagnoses as coefficients for cost confirmed significance and robustness of the model. Conclusion: Cost of care for preterm infant is expensive, and significantly increases with prematurity complication. Interventions to reduce those complications may enable resource allocation and better understanding of the needs of the neonatal health services.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".