Advocating for the inclusion of kidney health outcomes in neonatal research: best practice recommendations by the Neonatal Kidney Collaborative
Bibliographic record
Abstract
Acute kidney injury (AKI) occurs in nearly 30% of sick neonates. Chronic kidney disease (CKD) can be detected in certain populations of sick neonates as early as 2 years. AKI is often part of a multisystem syndrome that negatively impacts developing organs resulting in short- and long-term pulmonary, neurodevelopmental, and cardiovascular morbidities. It is critical to incorporate kidney-related data into neonatal clinical trials in a uniform manner to better understand how neonatal AKI or CKD could affect an outcome of interest. Here, we provide expert opinion recommendations and rationales to support the inclusion of short- and long-term neonatal kidney outcomes using a tiered approach based on study design: (1) observational studies (prospective or retrospective) limited to data available within a center's standard practice, (2) observational studies involving prospective data collection where prespecified kidney outcomes are included in the design, (3) interventional studies with non-nephrotoxic agents, and (4) interventional studies with known nephrotoxic agents. We also provide recommendations for biospecimen collection to facilitate ancillary kidney specific research initiatives. This approach balances the costs of AKI and CKD ascertainment with knowledge gained. We advocate that kidney outcomes be included routinely in neonatal clinical study design. Consistent incorporation of kidney outcomes across studies will increase our knowledge of neonatal morbidity.
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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 | 0.140 | 0.266 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".