Delivery room practices for extremely preterm infants: the harms of the gestational age label
Bibliographic record
Abstract
Interventions for extremely preterm infants bring up many ethical questions. To answer these questions, data are needed. The investigators of the Epipage-2 study have conducted a rigorous investigation: they report outcomes for all 2145 neonates born between 22 and 26 weeks of gestational age (GA) in France in 2011.1 Their primary outcome measure was the provision of life sustaining interventions in the delivery room and survival statistics. They show that, over a year in France, only one baby born before 24 weeks GA survived to neonatal intensive care unit (NICU) discharge. NICU admission was withheld for 96%, 91%, 38% and 8% of neonates at 22, 23, 24 and 25 weeks of GA. This data is not surprising, as the French policy recommends non-intervention for the smallest babies and practices conform to the policy. Six other European countries have similar non-treatment policies.2 National policies generally based their recommendations on local or national outcome data. But policies do not just reflect outcomes, they shape them. There is an iterative relationship between policies, guidelines and facts. Most other industrialised countries offer interventions at 23 weeks and, in those countries, many more such babies survive and most survivors do not have severe impairments. Generally, paediatricians and policy makers favour treatments that improve survival rates. Treatment for extremely preterm infants is the exception to this general rule. There are three common arguments …
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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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.020 | 0.028 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".