Bibliographic record
Abstract
The rate of multiple pregnancy has increased in developed countries, a finding usually attributed to more widespread use of assisted reproductive technologies. Multiple pregnancies are associated with a greater risk of pregnancy complications, including intrauterine growth restriction of one or more of the fetuses, vascular communications within a shared monochorionic placenta and premature delivery. Surviving infants are at significantly greater risk of developing cerebral palsy due to a combination of a higher proportion of them being preterm or of low birth weight, and complications associated with chorionicity. These infants are also at greater risk for abnormal cognitive development and learning disabilities for the same reasons. Parenting styles and family dynamics may also differ with multiples compared with singletons, which may affect long-term behaviour and development.Thus, infants of multiple pregnancies should receive careful neurodevelopmental follow-up. For larger, lower risk infants, this follow-up may be provided by general paediatricians within the community. However, for infants with birth weights of less than 1000 g or with a complicated antenatal or neonatal course, follow-up should be in a high-risk neonatal follow-up clinic with appropriate multidisciplinary support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".