Essential variables for reporting research studies on fetal growth restriction: a Delphi consensus
Bibliographic record
Abstract
OBJECTIVE: To determine, by expert consensus using a Delphi procedure, a minimum reporting set of study variables for fetal growth restriction (FGR) research studies. METHODS: A panel of experts, identified based on their publication record as lead or senior author of studies on FGR, was asked to select a set of essential reporting study parameters from a literature-based list of variables, utilizing the Delphi consensus methodology. Responses were collected in four consecutive rounds by online questionnaires presented to the panelists through a unique token-secured link for each round. The experts were asked to rate the importance of each parameter on a five-point Likert scale. Variables were selected in the three first rounds based on a 70% threshold for agreement on the Likert-scale scoring. In the final round, retained parameters were categorized as essential (to be reported in all FGR studies) or recommended (important but not mandatory). RESULTS: Of the 100 invited experts, 87 agreed to participate and of these 62 (71%) completed all four rounds. Agreement was reached for 16 essential and 30 recommended parameters including maternal characteristics, prenatal investigations, prenatal management and pregnancy/neonatal outcomes. Essential parameters included hypertensive complication in the current pregnancy, smoking, parity, maternal age, fetal abdominal circumference, estimated fetal weight, umbilical artery Doppler (pulsatility index and end-diastolic flow), fetal middle cerebral artery Doppler, indications for intervention, pregnancy outcome (live birth, stillbirth or neonatal death), gestational age at delivery, birth weight, birth-weight centile, mode of delivery and 5-min Apgar score. CONCLUSIONS: We present a list of essential and recommended parameters that characterize FGR independent of study hypotheses. Uniform reporting of these variables in prospective clinical research is expected to improve data quality, study consistency and ultimately our understanding of FGR. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.
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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 | 0.512 | 0.429 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".