The impact of high intensity care around birth on long-term neurodevelopmental outcomes
Bibliographic record
Abstract
BACKGROUND: An equitable and affordable healthcare system requires a constant search for the optimal way to deliver increasingly expensive neonatal care. Therefore, evaluating the impact of hospital intensity around birth on long-term health outcomes is necessary if we are to assess the value of high intensity neonatal care against its costs. METHODS: This study exploits uneven geographical distribution of high intensity birth hospitals across Canada to generate comparisons across similar Cerebral Palsy (CP) related births treated at hospitals with different intensities. We employ a rich dataset from the Canadian Multi-Regional CP Registry (CCPR) and instrumental variables related to the mother's location of residence around birth. RESULTS: We find that differences in hospitals' intensities are not associated with differences in clinically relevant, long-term CP health outcomes. CONCLUSIONS: Our results suggest that existing matching mechanism of births to hospitals within large metropolitan areas could be improved by early detection of high risk births and subsequent referral of these births to high intensity birthing centers. Substantial hospitalization costs might be averted to Canadian healthcare system ($16 million with a 95% CI of $6,131,184 - $24,103,478) if CP related births were assigned to low intensity hospitals and subsequently transferred if necessary to high intensity hospitals.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| 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".