Long-Term Kidney Outcomes after Pediatric Acute Kidney Injury: Future Studies Need to Explore Further
Bibliographic record
Abstract
We appreciate the work by Robinson et al.,1 published in JASN, and we read it with great interest. The authors conducted the study on a population-based, propensity score–matched cohort involving 4173 hospitalized pediatric AKI survivors and 16,337 matched hospitalized comparators without AKI. Over a median 10-year follow-up, their noteworthy findings indicated that pediatric AKI survivors had significantly higher risks of long-term major adverse kidney events, long-term KRT, CKD, hypertension, and subsequent AKI, but not death. The authors provided new insights into the management of pediatric AKI patients' prognosis; however, several concerns warrant attention. Primarily, when the authors calculated the propensity scores for AKI, the model included baseline sociodemographic characteristics, index hospitalization characteristics, preexisting comorbidities, and other potential confounding factors that might affect the relationship between AKI and long-term major adverse kidney events. However, we believe that future studies on AKI should not overlook the effect of certain nephrotoxic drugs or those that may cause kidney damage, such as chemotherapeutic agents, nonsteroidal anti-inflammatory drugs, proton pump inhibitors, antibiotics, and antivirals, on the development of AKI.2 Furthermore, the authors redefined AKI based on serum creatinine in the sensitivity analysis, and the results remained consistent. Notably, for neonates (accounting for 16.8% of this study), neonatal serum creatinine initially reflected the maternal values and decreased in the subsequent several weeks.3 A previous study has demonstrated that serum cystatin C was a more robust and sensitive biomarker than serum creatinine for detecting neonatal AKI.4 Therefore, we call for more studies in the future that define neonatal AKI on the basis of cystatin C and explore its long-term prognosis. In addition, because AKI was defined using Canadian health administrative diagnostic codes in this study, they only briefly explored the association between the presence of AKI and long-term outcomes. However, stratifying AKI by severity could provide a deeper understanding of the relationship between different AKI stages and their long-term outcomes, which will better align with the principles of precision medicine. Additional research is needed to elucidate these differences. Overall, the findings are highly constructive and insightful, and we believe that future studies can further address the aforementioned limitations and validate these findings.
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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 | 0.048 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.015 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".