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Record W3019000609 · doi:10.1097/md.0000000000019767

Effects of angiotensin-converting enzyme inhibitors or angiotensin receptor blockers on all-cause mortality, cardiovascular death, and cardiovascular events among peritoneal dialysis patients

2020· article· en· W3019000609 on OpenAlexaff
Surapon Nochaiwong, Chidchanok Ruengorn, Pajaree Mongkhon, Kednapa Thavorn, Ratanaporn Awiphan, Kajohnsak Noppakun, Surachet Vongsanim, Wilaiwan Chongruksut, Brian Hutton, Manish M. Sood, Greg Knoll

Bibliographic record

VenueMedicine · 2020
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersChiang Mai University
KeywordsMedicineCochrane LibraryFunnel plotInternal medicineMeta-analysisPopulationPeritoneal dialysisPublication biasRandomized controlled trialIntensive care medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Based on the International Society for peritoneal dialysis (PD) recommendations, blockade of renin-angiotensin systems with an angiotensin-converting enzyme inhibitors (ACEI) and angiotensin receptor blockers (ARB) improves residual kidney function in PD patients. However, the long-term effectiveness of ACEI/ARB use in PD patients has not been fully elucidated. We, therefore, intend to perform a systematic review and meta-analysis to summarize the effects of ACEI/ARB use on long-term mortality, cardiovascular outcomes, and adverse events among PD patients. METHODS: This systematic review will include both randomized controlled trials and non-randomized studies in adult PD patients. We also plan to incorporate data from our cohort study in Thai PD population into this review. We will search PubMed, Medline, EMBASE, Cochrane Library, Web of Science, Scopus, CINAHL, and grey literature from inception to February 29, 2019, with no language restrictions. The process of study screening, selection, data extraction, risk of bias assessment, and grading the strength of evidence will be performed independently by a pair of reviewers. Any discrepancy will be resolved through a team discussion and/or consultation with the third reviewer. The pooled effects estimate and 95% confidence intervals will be estimated using DerSimonian-Laird random-effects models. Heterogeneity will be assessed by the Cochran Q test, I index and tau-squared statistics. The funnel plots along with the Begg and Egger test and trim and fill method will be performed to investigate any evidence of publication bias. Preplanned subgroup analyses and random-effects univariate meta-regressions will be performed to quantify the potential sources of heterogeneity based on studies- and patient-characteristics. RESULTS: This will be the first systematic review and meta-analysis to summarize the long-term effectiveness of renin-angiotensin system inhibitors in PD populations. CONCLUSION: In summary, this systematic review and meta-analysis will summarize the effectiveness of ACEI/ARB on long-term mortality, cardiovascular outcomes, and adverse events among adult PD patients by integrated all available evidences. ETHICS AND DISSEMINATION: Based on the existing published data, an ethical approval is not required. The findings will be disseminated through scientific meetings and publications in peer-reviewed journals.PROSPERO registration number: CRD42019129492.

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)0.012
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.012
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.019
Bibliometrics0.0050.004
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
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 head0.023
GPT teacher head0.248
Teacher spread0.225 · 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 designObservational
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

Citations2
Published2020
Admission routes1
Has abstractyes

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