The influence of biological sex on diagnostic markers of acute kidney injury in acute-on-chronic liver failure: insights from a single-centre tertiary care study
Bibliographic record
Abstract
Biological sex has a profound impact on disease severity, outcomes and diagnosis yet, its role in clinical disease is insufficiently explored. Acute on chronic liver failure (ACLF) is associated with high mortality and multiple organ dysfunctions, where acute kidney injury (AKI) significantly worsens prognosis. Here we investigated the impact of sex on the diagnostic parameters used for severity grading in ACLF. We enrolled 1,134 ACLF patients, and shortlisted 757 patients (636 males, 121 females) admitted to All India Institute of Medical Sciences, New Delhi, between 2016 and 2023. ACLF-AKI was defined and staged according to International Club of Ascites criteria. The impact of sex on baseline clinical parameters, AKI incidence, and progression were assessed using the statistical tools IBM SPSS 26.0 and GraphPad Prism 8.0. Males exhibited a higher incidence of AKI (48.34%) compared to females (28.09%). However, no significant sex-based differences were observed in AKI stages. Males also had an overall high absolute value of sCr and blood urea compared to females. However, female ACLF patients who developed AKI exhibited a significantly higher ΔsCr levels compared to males (p = 0.003). Kaplan-Meier analysis revealed that males developed AKI significantly faster (median 2 days) than females (median 5 days) during the first week of hospitalization. In conclusion, sex-based differences were observed in the widely used diagnostic criteria of sCr and ΔsCr for AKI in patients with ACLF. Although these findings are preliminary our results reveal sex-specific differences in sCr-based AKI diagnosis and risk stratification in ACLF which warrant further validation in prospective multi-centric cohort studies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".