Bibliographic record
Abstract
Sir, In reply to the comments by Morgan, we generally concur and offer the following additional commentary. We agree that the addition of patient weight, hourly urine output and baseline serum creatinine as core variables to the Australia New Zealand Intensive Care Society (ANZICS) Adult Patient Database (APD) would have tremendous value and certainly advance its capability for additional evaluation of acute kidney injury (AKI) and other kidney-related issues. At the time of analysis, however, these variables were not available [1]. Accordingly, assumptions about the data and their application to calculate the RIFLE categories were necessary. We recognize these assumptions potentially introduce some misclassification of the cohort and, as expected, influence incidence and outcome estimates. We, however, contend that any bias introduced due to misclassification resulting from these assumptions was likely to be balanced given they were applied systematically across the entire cohort. Moreover, the validated collection of these variables (i.e. patient weight, urine output, baseline serum creatinine) can be problematic. For example, the measurement of weight in critically ill patients is highly variable and context specific (i.e. ideal versus actual). Accurate estimates of pre-hospitalization baseline creatinine (or estimated glomerular filtration rate), in particular for those with chronic kidney disease, in critically ill patients are often impossible. Moreover, values at the time of ICU admission may be grossly modified by factors such as acute resuscitation. Likewise, the urine output can be modified by factors independent of kidney injury or function (i.e. fluid therapy, diuretic therapy). However, we also recognize that while the urine output criteria proposed for the RIFLE classification likely have significance, they have yet to be prospectively evaluated and validated. We appropriately acknowledge and discuss these limitations in our manuscript [2,3]. We are further reassured, however, by additional epidemiologic investigations that have found relative consistency in incidence rates and effect estimates for AKI and associated clinical outcomes with the RIFLE criteria (many having modified the original RIFLE criteria or omitting the urine output criteria altogether) [4,5]. We contend that our study is strengthened by inclusion of a very large heterogeneous cohort (over 120 000 critically ill patients) from multiple centres across Australia. As such, in the very least, it provides a broad estimate of the burden of early AKI (within 24 h of ICU admission) in critically ill patients. Finally, we certainly agree and would welcome additional prospective evaluation of the performance of the RIFLE criteria in similar cohorts of critically ill patients. Conflict of interest: 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 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.044 | 0.044 |
| Insufficient payload (model declined to judge) | 0.022 | 0.016 |
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".