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Record W4223561836 · doi:10.3389/fmed.2022.861013

Physical Activity and Mortality in Patients With Chronic Kidney Disease: A Protocol for Systematic Review and Dose-Response Meta-Analysis

2022· article· en· W4223561836 on OpenAlexaboutno aff
Fan Zhang, Hui Wang, Liuyan Huang, Yan Bai

Bibliographic record

VenueFrontiers in Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
FundersLonghua Hospital Shanghai University of Traditional Chinese MedicineShanghai University of Traditional Chinese Medicine
KeywordsMedicineMeta-analysisCochrane LibraryKidney diseasePublication biasCohortCohort studySubgroup analysisGrading (engineering)Internal medicineMeta-regressionMEDLINESystematic reviewPhysical therapy

Abstract

fetched live from OpenAlex

Objective: To examine the dose-response associations between total physical activity, different intensity of physical activity, and all-cause mortality in patients with chronic kidney disease (CKD). Methods and Analysis: PubMed, Embase, Web of Science, and the Cochrane library will be searched from inception to June 2022. Only cohort studies assessing physical activity associations with all-cause mortality among CKD patients will be considered for inclusion. The quality of included cohort studies will be evaluated according to the Newcastle-Ottawa Scale (NOS). The robust error meta-regression (REMR) model will be used to establish dose-response relationships between physical activity and mortality. Additional statistical analysis including Egger's test, subgroup analysis, sensitivity analysis. The strengths of evidence will be evaluated with the Grading of Recommendation, Assessment, Development, and Evaluation approach. Ethics and Dissemination: Ethics approval is not required as no private information from individuals is collected. PROSPERO Registration Number: CRD 42021283630.

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)0.062
metaresearch head score (Gemma)0.098
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.062
Threshold uncertainty score0.327

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0620.098
Meta-epidemiology (narrow)0.0050.004
Meta-epidemiology (broad)0.0180.029
Bibliometrics0.0100.010
Science and technology studies0.0030.003
Scholarly communication0.0060.005
Open science0.0040.004
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0410.004

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 head0.027
GPT teacher head0.337
Teacher spread0.310 · 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 designNot applicable
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

Citations3
Published2022
Admission routes1
Has abstractyes

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