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Record W4200545714 · doi:10.21203/rs.3.rs-1064753/v1

How Does HbA1c Predict Mortality and Readmission in Patients with Heart Failure? A Protocol for Systematic Review and Meta-analysis

2021· preprint· en· W4200545714 on OpenAlexaboutno aff
Jun‐Peng Xu, Rui‐Xiang Zeng, Xiao‐Yi Mai, Wenjun Pan, Yuzhuo Zhang, Minzhou Zhang

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsnot available
FundersGuangdong Provincial Hospital of Traditional Chinese Medicine
KeywordsMeta-analysisProtocol (science)Heart failureMedicineInternal medicineComputer scienceIntensive care medicineCardiologyAlternative medicinePathology

Abstract

fetched live from OpenAlex

Abstract BackgroundAccumulating evidence suggests that HbA1c levels, a common clinical indicator of chronic glucose metabolism over the preceding 2-3 months, are independent risk factor for cardiovascular disease, including heart failure. The aim of this protocol is to conduct a systematic review and meta-analysis to assess the possible predictive value of HbA1c on mortality and readmission in patients with heart failure.MethodsA systematic and comprehensive search will be performed using PubMed, Embase, Central and other databases before August 2021 to identify relevant trials. All-cause mortality is the pre-specified primary endpoint. Cardiovascular death and heart failure readmission are secondary interested endpoints. We will only include prospective and retrospective cohort trials and place no restrictions on the language, race, region and publication period. The Newcastle-Ottawa Scale will be used to assess the quality of each trial included. If there are sufficient trials, we will conduct meta-analysis with pooled relative risks and corresponding 95% confidence interval to evaluate the possible predictive value of HbA1c on mortality and readmission. Otherwise, we will undertake a narrative synthesis. Heterogeneity and publication bias will be assessed. If heterogeneity is significant among included trails, a sensitivity analysis or subgroup analysis will be used to explore the source of heterogeneity, such as diverse types of heart failure or patients with diabetes and non-diabetes. Also, we will conduct meta-regression to examine the time-effect and treatment-effect modifiers on all-cause mortality compared between different quantile of HbA1c levels. Finally, a restricted cubic spline model may be used to explore the dose-response relationship between HbA1c and adverse outcomes.DiscussionThis planned analysis is anticipated to identify the predictive value of HbA1c on mortality and readmission in patients with heart failure. Improved understanding of different HbA1c levels and their specific effect on diverse types of heart failure or patients with diabetes and non-diabetes is expected to be figured out. Also, a dose-response relationship or optimal range of HbA1c will be determined to instruct clinicians and patients.PROSPERO registration detailsCRD42021276067

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)0.079
metaresearch head score (Gemma)0.139
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 score0.079
Threshold uncertainty score0.416

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0790.139
Meta-epidemiology (narrow)0.0060.004
Meta-epidemiology (broad)0.0230.041
Bibliometrics0.0120.011
Science and technology studies0.0030.003
Scholarly communication0.0080.006
Open science0.0060.005
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0480.005

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 head0.129
GPT teacher head0.444
Teacher spread0.315 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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