Bibliographic record
Abstract
Sir, We thank Dr Liborio for the thoughtful evaluation of acute kidney injury (AKI) definition in children. Indeed, the author is correct in noting that the relationship between estimated glomerular filtration rate (eGFR) and serum creatinine (SCr) is not quite linear; a ‘linear relationship’ is indirectly assumed by equivocating pRIFLE R, I and F AKI to AKIN Stages 1, 2 and 3 AKI, respectively. Rather, the GFR–SCr relationship is closer to that of a logarithmic curve. The author is also correct in noting that if we consider the pRIFLE definition as the ‘gold standard’ for defining AKI, the AKIN definition would misclassify all patients with a 33–49% SCr rise as not having AKI (when they do by pRIFLE) and would misclassify all patients with a three to four times SCr rise from baseline as having AKI (when they do not by pRIFLE). However, several things must be considered, including issues related to physiology of acute illness and the history of AKI definition, which may question the extent to which the pRIFLE definition really is a better ‘gold standard’ than the AKIN definition is. Assuming that pRIFLE is a ‘better’ AKI definition than the AKIN also assumes that the relationship between SCr and eGFR is similar in hospitalized patients as it is in stable, non-hospitalized patients, from which these equations were derived. Given the fluid shifts, recent changes in nutrition status in chronically ill patients, medication provided and other factors, it is quite likely that this is not the case. However, a benefit of the pRIFLE definition (which is based on eGFR) does allow for more meaningful interpretation of renal function in children, in whom SCr alone is difficult to interpret across age groups due to its strong relation to muscle mass. From a historical perspective, the original RIFLE criteria derived for adults included the possibility of using either eGFR or SCr criteria to define AKI [ 1 ]. Subsequently, the pRIFLE definition [ 2 ] was proposed prior to the AKIN consensus definition [ 3 ]; this likely indirectly led to the assumption that pRIFLE is a better definition against which all future child AKI definitions must be compared. The decision to use change in eGFR using the cut-offs of 25, 50 and 75%, to define the pRIFLE (as opposed to change in SCr) was somewhat empirical and not based on evidence that these cut-offs are in fact associated with worse outcomes in a linear graded fashion. It is possible that the pRIFLE R criterion (25% decrease in eGFR or a 1.33 × increase in SCr) is too sensitive, from the perspective of evaluating the relationship of AKI with outcome; similarly, it is possible that there is little difference between a three versus four times increase in SCr, when considering AKI-outcome associations. Ultimately, the only way to answer the question as to which definition is better and what SCr cut-offs are most important, is to determine whether these minor differences actually lead to change in the conclusion of AKI-outcome associations. AKI biomarker studies, which provide information on actual renal tubular injury, may also help to decipher this issue. Until then, we propose that it is perhaps wiser to be consistent with AKI definition and that studies always clearly specify what definition is used, how it is used and how baseline SCr is defined, so that results may be compared across studies and a clearer understanding of how to best define AKI can be inferred. Finally, when considering that in recent years, studies of AKI in adults appear to be more commonly using the AKIN staging definition, it may be worthwhile to adhere to or at least describe this definition in children as well, particularly when attempting to place pediatric AKI findings in the greater context of AKI research. Conflict of interest statement . None declared.
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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 | 0.005 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.045 | 0.044 |
| Insufficient payload (model declined to judge) | 0.023 | 0.017 |
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".