Bibliographic record
Abstract
The main problem with preterm labour is our lack of progress in the successful management of this condition. We need to reassess our approach to this problem because preterm labour is not a disease, but an event, which may result from multiple independent pathways. This problem has also been affected significantly by medical advances such as infertility treatments and changes in neonatal survival at the limit of viability. The specific challenges that we face in managing preterm labour include: problems with definition; aetiology, including genetic and infection components; diagnostic problems, such as true versus false labour and role of cervical length and fetal fibronectin; and specific interventions according to the antepartum, intrapartum and postpartum challenges. In order to address the main issue, and make future progress in the management of preterm labour, we should consider the implementation of a 'Postpartum Preterm Labour Diagnostic Workup Protocol'. These data/workup results could be entered on web-based databases for each preterm labour 'event'. An international research team could analyse data relating to specific aetiological patterns and subgroup analyses, leading to the collaborative development of 'aetiology specific' management modalities. This approach requires a close collaboration between clinicians and researchers, in order to make significant progress in this difficult area, and ultimately improve perinatal outcomes.
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 | 0.049 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".