Bibliographic record
Abstract
Spontaneous onset of labour at term is the most desirable finale of pregnancy, heralding, as it usually does, the maturation of both fetal and maternal systems necessary for childbirth. Induction of labour is second best and this intervention is justified only for clear maternal, fetal or combined reasons. In induction of labour, uterine contractions are initiated by mechanical and/or pharmacological methods with the aim of achieving vaginal delivery. The options for induction of labour available in each centre should be discussed with the woman, including the possible success and failure rates and complications. The conditions for which induction is carried out will vary and depend on the medical or obstetric condition of the mother, the fetal condition, the knowledge and experience of the clinician, the willingness of the woman to undergo the procedure and the facilities available. Hence, the rates of induction of labour in the UK vary from 10% to 20%. Indications and contraindications for induction of labour There are several indications for induction of labour, the most common being: • prolonged pregnancy (> 41 weeks) • preterm prelabour rupture of membranes • fetal growth restriction • maternal conditions such as pre-eclampsia, diabetes, cholestasis or systemic lupus erythematosus • abnormal antenatal fetal surveillance tests.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.079 | 0.052 |
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".