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Record W2140840063 · doi:10.1093/ndt/gfm818

N-Acetylcysteine does not artifactually lower plasma creatinine concentration

2008· article· en· W2140840063 on OpenAlexaff
Michael Haase, Anja Haase‐Fielitz, S Ratnaike, Michael C. Reade, Sean M. Bagshaw, Stanislao Morgera, Duska Dragun, Rinaldo Bellomo

Bibliographic record

VenueNephrology Dialysis Transplantation · 2008
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineAcetylcysteineCreatinineAcute kidney injuryIntensive care medicineInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND: All randomized controlled trials of N-acetylcysteine (NAC) in contrast media-induced nephropathy used creatinine as a marker of renal function. However, it has been suggested that NAC may lower plasma creatinine levels independent of any effects on glomerular filtration rate (GFR). METHODS: At a tertiary hospital 110 cardiac surgical patients were randomly allocated to peri-operative infusion of NAC (300 mg/kg over 24 h, N = 30) or placebo (N = 80). We compared the plasma concentrations of creatinine, cystatin C and urea, the plasma creatinine/plasma cystatin C ratio and the estimated GFR at baseline and at 24 and 72 h after commencement of the infusion. We measured urinary creatinine concentration at 24 h. RESULTS: At baseline, the plasma creatinine/plasma cystatin C ratio did not differ between the NAC and placebo group (0.90 versus 0.92; P = 0.94). There was no significant difference in the plasma creatinine/plasma cystatin C ratio for the NAC and placebo group either during or after NAC infusion at 24 h (1.03 versus 1.00; P = 0.78) and 72 h (0.94 versus 0.89; P = 0.09). Those allocated to NAC showed no difference in urinary creatinine excretion when compared to placebo (P = 0.24). CONCLUSIONS: The results of our study do not demonstrate that NAC artifactually lowers creatinine measured using the Jaffé method. (ClinicalTrials.gov, NCT00332631, NCT00334191).

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.284
Teacher spread0.263 · 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.

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

Citations35
Published2008
Admission routes1
Has abstractyes

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