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Record W4397040681 · doi:10.1681/asn.20233411s127c

Cystatin C-Defined AKI in Children Treated with Cisplatin

2023· article· en· W4397040681 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the American Society of Nephrology · 2023
Typearticle
Languageen
FieldMedicine
TopicBiomedical Research and Pathophysiology
Canadian institutionsUniversity of British ColumbiaMcGill University Health CentreUniversity of Toronto
Fundersnot available
KeywordsCystatin CCisplatinMedicineUrologyInternal medicineAcute kidney injuryOncologyIntensive care medicineRenal functionChemotherapy

Abstract

fetched live from OpenAlex

Background: Cisplatin chemotherapy causes acute kidney injury (AKI). Early AKI detection and mitigation may prevent long-term sequelae. Serum Cystatin C (CysC) may be an early AKI biomarker compared to SCr and is not impacted by muscle mass. We: 1) compared AKI defined by acute CysC change (CysC-AKI) to SCr-defined AKI (SCr-AKI) in children treated with cisplatin; 2) evaluated the relation of a) urine neutrophil gelatinase-associated lipocalin [NGAL] and kidney injury molecule-1 [KIM-1] with CysC-AKI and SCr-AKI and b) of CysC with SCr-AKI. Methods: 12-centre prospective study of 159 children receiving cisplatin. Exclusions: CKD, missing CysC or baseline SCr. At early cisplatin infusions (1st or 2nd cisplatin cycle), SCr, CysC, urine NGAL and KIM-1 were measured pre-/post-infusion and at discharge. Outcomes: KDIGO SCr-AKI (yes/no). CysC-AKI was ≥1.5x rise from baseline or CysC-GFR<35ml/min/1.73m2. We calculated kappa statistic for agreement of AKI definitions, Mann-Whitney U test to compare biomarker levels by AKI, and area under the curve (AUC) for a) NGAL/KIM-1 to predict CysC-AKI and SCr-AKI and b) for CysC to predict SCr-AKI. Results: 154 children included (51% male, median age [IQR] 5.6 [2.4-11.7] years). SCr-AKI 7/154 (5%), CysC-AKI 25/154 (16%), 83% agreement (kappa=0.13, p=0.025). There was no significant difference in NGAL or KIM-1 concentrations by SCr-AKI or CysC-AKI at all time points (Figure1). Pre- and post-infusion NGAL performed similarly to predict SCr-AKI and CysC-AKI (Figure1). KIM-1 poorly predicted SCr-AKI and CysC-AKI (Figure1). Pre-infusion CysC had the highest AUC for predicting SCr-AKI (AUC 0.56).Figure 1:: Comparison of urinary biomarkers (NGAL/KIM-1) by SCr-AKI and CysC-AKI in children treated with clsplatin.Conclusions: CysC-AKI had a low level of agreement with SCr-AKI in children treated with cisplatin. CysC was not a strong predictor of SCr-AKI in this population. Future studies with more measurement time points are needed to determine if this is possibly due to earlier rise of CysC compared to SCr. NGAL and KIM-1 did not strongly predict AKI by either definition.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.599
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.301
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it