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Record W2893547655 · doi:10.1177/2054358118801017

The Effect of N-Acetylcysteine on Creatinine Measurement: Protocol for a Systematic Review

2018· review· en· W2893547655 on OpenAlexaffabout
Johnny W. Huang, Owen Clarkin, Christopher R. McCudden, Ayub Akbari, Benjamin J.W. Chow, Wael Shabana, Salmaan Kanji, Alexandra Davis, Swapnil Hiremath

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2018
Typereview
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineKidney diseaseRenal functionCreatinineAcute kidney injuryAcetylcysteineInternal medicineCystatin CNephrologyRandomized controlled trialContext (archaeology)Prospective cohort studySystematic reviewKidneyNephropathyIntensive care medicineUrologyMEDLINEEndocrinologyDiabetes mellitusAntioxidant

Abstract

fetched live from OpenAlex

BACKGROUND: N-acetylcysteine (NAC) is an antioxidant which can regenerate glutathione and is primarily used for acetaminophen overdose. It is also a potential therapy to prevent iatrogenic acute kidney injury or slow the progression of chronic kidney disease. It has been considered in this context by many studies with mixed results. Notably, a biological-mechanism rationale for a protective effect of NAC has never been adequately reported. Among conflicting reports, there appears to be evidence that NAC may artificially lower measured serum creatinine without improving kidney function, potentially by assay interference. Given these mixed results, a systematic review of the literature will be conducted to determine whether there is an effect of NAC on kidney function measured with serum creatinine. OBJECTIVE: To determine the effect of NAC on kidney function. DESIGN: A systematic review and meta-analysis. SETTINGS: Prospective studies, with administration of NAC, in the absence of any other change in kidney function (such as contrast administration or surgery). PATIENTS: Adult humans aged 18 years old or more, either healthy volunteers or with chronic kidney disease, were administered with NAC. Populations having little to no kidney function such as in end-stage kidney disease will be excluded. MEASUREMENTS: Serum creatinine and/or cystatin C measurements before and after NAC administration. METHODS: An information specialist will assist in searching MEDLINE, EMBASE, and the Cochrane CENTRAL databases to identify all study types including randomized controlled trials, and prospective cohort studies reporting change in serum creatinine after NAC administration. Two reviewers will independently screen the titles and abstracts of the studies obtained from the search using predefined inclusion criteria and will then extract data from the full texts of selected studies. The weighted mean difference will be calculated for change in creatinine with NAC, using random-effects analysis. Quality assessment will be done with the Cochrane Risk of Bias tool for randomized trials and the Newcastle-Ottawa Scale for observational studies. RESULTS: The outcome of interest is kidney function as reported by either change in serum creatinine and/or serum cystatin C measurement for randomized trials or comparing baseline (pre-NAC dose) values and those following the NAC dose. LIMITATIONS: Possible heterogeneity and publication bias and lack of mechanistic data. CONCLUSIONS: This systematic review will provide a synthesis of current evidence on the effect of NAC on serum creatinine measurement. These findings will provide clinicians with guidelines and serve as a strong research base for future studies in this field. SYSTEMATIC REVIEW REGISTRATION: This review is registered with PROSPERO, CRD42017055984.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.053
metaresearch head score (Gemma)0.086
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.053
Threshold uncertainty score0.280

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0530.086
Meta-epidemiology (narrow)0.0060.005
Meta-epidemiology (broad)0.0240.020
Bibliometrics0.0100.011
Science and technology studies0.0040.004
Scholarly communication0.0070.008
Open science0.0050.004
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0530.006

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 head0.088
GPT teacher head0.447
Teacher spread0.360 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreProtocol

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

Citations5
Published2018
Admission routes2
Has abstractyes

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