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Record W3199484988 · doi:10.1097/md.0000000000027149

Coffee consumption is associated with a decreased risk of incident chronic kidney disease

2021· article· en· W3199484988 on OpenAlexaboutno aff
Yudong Li, Wenchang Li, Yisheng Lu, Jing Zhang

Bibliographic record

VenueMedicine · 2021
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineObservational studyCochrane LibraryKidney diseaseMEDLINESystematic reviewProtocol (science)DiseaseMeta-analysisInternal medicineIntensive care medicineAlternative medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Recent studies have suggested a renal protective effect of coffee consumption against development of chronic kidney disease (CKD), although the results remain inconclusive. We performed a protocol for systematic review and meta-analysis to comprehensively investigate this association by summarizing all available data. METHODS: An all-round retrieval will be performed in 5 electronic journal databases from their inception to June 2021, which comprise Medline, PubMed, Embase, ScienceDirect, and the Cochrane Library. The following key words were used on combination with Boolean operators AND or OR: "coffee," "caffeine," "renal insufficiency," "chronic kidney diseases," "chronic renal diseases." Two authors completed the quality assessment using the Newcastle-Ottawa Scale for observational studies. The meta-analysis was conducted using Review Manager 5.3 software from the Cochrane Collaboration (London, UK). RESULTS: The findings of this study will be submitted to peer-reviewed journals for publication. CONCLUSION: Coffee consumption may be associated with a lower risk of incident CKD.

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)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0040.000

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 head0.027
GPT teacher head0.321
Teacher spread0.294 · 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 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

Citations4
Published2021
Admission routes1
Has abstractyes

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