Bibliographic record
Abstract
Background: Home birth is as old as humanity, but still most middle- and high-income countries consider hospitals as the safest birth settings, as complications regarding birth are highly unpredictable. Despite this there are a few countries in which home birth in integrated into official healthcare system (the Netherlands, United Kingdom, Canada etc.). Home births can be divided into unplanned and planned, and the latter can be further categorized by the presence of the birth attendants. This review focuses on planned home births, which are differently represented throughout the world. In the United States 0.6-1.0% of all children are born at home, in the United Kingdom 2-3%, in Canada 1.6% and in the Netherlands 20-30%. For Slovenia, the number of planned home births is unknown; however, in 2010 0.1% of children were born outside medical facilities. Conclusions: The safety of home birth in still under the debate. While research confirms smaller number of obstetric interventions and some complications in mothers who give birth at home, the data regarding the neonatal and perinatal mortality and morbidity is still conflicting. This confirms the need for large multicentric trials in this field. Current home birth guidelines emphasize that women should be well informed regarding the possible advantages and disadvantages of home births. In addition, the emphasis is on definition of selection criteria for home birth, indications for intrapartal transfer to the hospital and appropriate education of birth attendants.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".