Prospective assessment of inter-rater reliability of a neonatal adverse event severity scale
Bibliographic record
Abstract
Introduction: To ensure the quality of clinical trial safety data, universal data standards are required. In 2019 the International Neonatal Consortium (INC) published a neonatal adverse event severity scale (NAESS) to standardize the reporting of adverse event (AE) severity. In this study the reliability of AE severity grading with INC NAESS was prospectively assessed in a real-world setting. Methods: Severity of AEs was assessed by two independent observers at each of four centers across the world. In each center two series of 30 neonatal adverse events were assessed by both observers: in a first phase with a generic (Common Terminology Criteria for Adverse Events, CTCAE) severity scale not specific to neonates, and in a second phase with INC NAESS (after a structured training). Intraclass correlation coefficients (ICC) were calculated to express inter-rater agreement in both phases, and bootstrap sampling was used to compare them. Results: 120 AEs were included in each of both phases. The ICC with the use of INC NAESS in phase 2 was 0.69. This represents a significant but modest improvement in comparison to the initial ICC of 0.66 in phase 1 (confidence interval of ratio of ICC in phase 2 to phase 1 = 1.005–1.146; excludes 1). The ICC was higher for those AEs for which a diagnosis specific AE severity table was available in INC NAESS (ICC 0.80). Discussion: Good inter-rater reliability of the INC NAESS was demonstrated in four neonatal intensive care units (NICUs) across the globe. The ICC is comparable to what is reported for scales with similar purposes in different populations. There is a modest, but significant, improvement in inter-rater agreement in comparison to the naïve phase without INC NAESS. The better performance when reviewers use AE-specific NAESS tables highlights the need to expand the number of AEs that are covered by specific criteria in the current version of INC NAESS.
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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 | 0.061 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".