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Record W2465162052 · doi:10.4137/bmi.s39199

Changes in Urinary and Serum Levels of Novel Biomarkers after Administration of Gadolinium-based Contrast Agents

2016· article· en· W2465162052 on OpenAlexaff
Habib Mawad, Louis‐Philippe Laurin, Jean‐François Naud, Nathalie Henley, Michel Vallée, Vincent Pichette, Martine Leblanc

Bibliographic record

VenueBiomarker Insights · 2016
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsCreatinineUrinary systemMedicineCystatin CRenal functionLipocalinNeutrophil gelatinase-associated lipocalinUrologyCystatinInternal medicineImpaired renal functionGadoliniumEndocrinologyChemistry

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of our study is to describe the changes in urinary and serum levels of novel biomarkers after gadolinium contrast administration in patients with normal renal function. METHODS: We measured four biomarkers in 28 volunteers: interleukin-18 (IL-18), N-acetyl-glucosaminidase (NAG), neutrophil gelatinase-associated lipocalin, and cystatin C. Urinary and serum samples were collected at 0, 3, and 24 hours following gadolinium administration. RESULTS: Baseline serum creatinine was 57.8 ± 34.5 µmol/L and remained stable. Urinary IL-18 levels increased significantly at three hours (10.7 vs. 7.3 ng/mg creatinine; P < 0.05). Similarly, urinary NAG levels increased significantly at three hours (3.9 vs. 2.2 IU/mg creatinine; P < 0.001). For both these markers, the difference was no longer significant at 24 hours. No statistically significant differences were observed for urinary and serum neutrophil gelatinase-associated lipocalin levels and for serum cystatin C levels. CONCLUSIONS: Urinary IL-18 and NAG levels increased transiently after administration of gadolinium-based contrast agents in patients with normal renal function.

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.

How this classification was reachedexpand

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.082
GPT teacher head0.345
Teacher spread0.262 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2016
Admission routes1
Has abstractyes

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