A core outcome set for pre‐eclampsia research: an international consensus development study
Bibliographic record
Abstract
OBJECTIVE: To develop a core outcome set for pre-eclampsia. DESIGN: Consensus development study. SETTING: International. POPULATION: Two hundred and eight-one healthcare professionals, 41 researchers and 110 patients, representing 56 countries, participated. METHODS: Modified Delphi method and Modified Nominal Group Technique. RESULTS: A long-list of 116 potential core outcomes was developed by combining the outcomes reported in 79 pre-eclampsia trials with those derived from thematic analysis of 30 in-depth interviews of women with lived experience of pre-eclampsia. Forty-seven consensus outcomes were identified from the Delphi process following which 14 maternal and eight offspring core outcomes were agreed at the consensus development meeting. Maternal core outcomes: death, eclampsia, stroke, cortical blindness, retinal detachment, pulmonary oedema, acute kidney injury, liver haematoma or rupture, abruption, postpartum haemorrhage, raised liver enzymes, low platelets, admission to intensive care required, and intubation and ventilation. Offspring core outcomes: stillbirth, gestational age at delivery, birthweight, small-for-gestational-age, neonatal mortality, seizures, admission to neonatal unit required and respiratory support. CONCLUSIONS: The core outcome set for pre-eclampsia should underpin future randomised trials and systematic reviews. Such implementation should ensure that future research holds the necessary reach and relevance to inform clinical practice, enhance women's care and improve the outcomes of pregnant women and their babies. TWEETABLE ABSTRACT: 281 healthcare professionals, 41 researchers and 110 women have developed #preeclampsia core outcomes @HOPEoutcomes @jamesmnduffy. [Correction added on 29 June 2020, after first online publication: the order has been corrected.].
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 | 0.393 | 0.406 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".