The neonatal adverse event severity scale: current status, a stakeholders' assessment, and future perspectives
Bibliographic record
Abstract
To support informed decisions on drug registration and prescription, clinical trials need tools to assess the efficacy and safety signals related to a given therapeutic intervention. Standardized assessment facilitates reproducibility of results. Furthermore, it enables weighted comparison between different interventions, instrumental to facilitate shared decisions. When focused on adverse events in clinical trials, tools are needed to assess seriousness, causality and severity. As part of such a toolbox, the international Neonatal Consortium (INC) developed a first version of the neonatal adverse event severity scale (NAESS). This version underwent subsequent validation in retro-and prospective trials to assess its applicability and impact on the inter-observer variability. Regulators, sponsors and academic researchers also reported on the use of the NAESS in regulatory documents, trial protocols and study reports. In this paper, we aim to report on the trajectory, current status and impact of the NAESS score, on how stakeholders within INC assess its relevance, and on perspectives to further develop this tool.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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; a candidate call from one teacher head, 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".