Strengthening Reporting of Neonatal Trials
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: There is variability in the selection and reporting of outcomes in neonatal trials with key information frequently omitted. This can impact applicability of trial findings to clinicians, families, and caregivers, and impair evidence synthesis. The Neonatal Core Outcomes Set describes outcomes agreed as clinically important that should be assessed in all neonatal trials, and Consolidated Standards of Reporting Trials (CONSORT)-Outcomes 2022 is a new, harmonized, evidence-based reporting guideline for trial outcomes. We reviewed published trials using CONSORT-Outcomes 2022 guidance to identify exemplars of neonatal core outcome reporting to strengthen description of outcomes in future trial publications. METHODS: Neonatal trials including >100 participants per arm published between 2015 to 2020 with a primary outcome included in the Neonatal Core Outcome Set were identified. Primary outcome reporting was reviewed using CONSORT 2010 and CONSORT-Outcomes 2022 guidelines by assessors recruited from Cochrane Neonatal. Examples of clear and complete outcome reporting were identified with verbatim text extracted from trial reports. RESULTS: Thirty-six trials were reviewed by 39 assessors. Examples of good reporting for CONSORT 2010 and CONSORT-Outcomes 2022 criteria were identified and subdivided into 3 outcome categories: "survival," "short-term neonatal complications," and "long-term developmental outcomes" depending on the core outcomes to which they relate. These examples are presented to strengthen future research reporting. CONCLUSIONS: We have identified examples of good trial outcome reporting. These illustrate how important neonatal outcomes should be reported to meet the CONSORT 2010 and CONSORT-Outcomes 2022 guidelines. Emulating these examples will improve the transmission of information relating to outcomes and reduce associated research waste.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.169 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".