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Record W4392509605 · doi:10.1186/s12882-024-03516-y

Consensus recommendations on fasting during Ramadan for patients with kidney disease: review of available evidence and a call for action (RaK Initiative)

2024· article· en· W4392509605 on OpenAlexaff
Yousef Boobes, Bachar Afandi, Fatima AlKindi, Ahmad Raed Tarakji, Saeed M Al Ghamdi, Mona Alrukhaimi, Mohamed Hassanein, Ali AlSahow, Riyad Said, Jafar Alsaid, Abdulkareem Alsuwaida, Ali A K Al Obaidli, Latifa Baynouna AlKetbi, Khaled Boubes, Nizar Attallah, Issa S Al Salmi, Yasser Abdelhamid, Nihal Bashir, Rania M Y Aburahma, Mohamed Hassan, Mohamed Amr Salah Al-Din Abd Al-Hakim

Bibliographic record

VenueBMC Nephrology · 2024
Typearticle
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsMcMaster University Medical Centre
Fundersnot available
KeywordsMedicineCall to actionNephrologyKidney diseaseMultidisciplinary approachFamily medicineDiseaseAlternative medicineAction (physics)Health careIntensive care medicineInternal medicineAdvertisingPathology

Abstract

fetched live from OpenAlex

Ramadan fasting (RF) involves abstaining from food and drink during daylight hours; it is obligatory for all healthy Muslims from the age of puberty. Although sick individuals are exempt from fasting, many will fast anyway. This article explores the impact of RF on individuals with kidney diseases through a comprehensive review of existing literature and consensus recommendations. This study was conducted by a multidisciplinary panel of experts.The recommendations aim to provide a structured approach to assess and manage fasting during Ramadan for patients with kidney diseases, empowering both healthcare providers and patients to make informed decisions while considering their unique circumstances.

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)0.042
metaresearch head score (Gemma)0.081
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: Review · Consensus signal: Review
Teacher disagreement score0.042
Threshold uncertainty score0.220

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.081
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.008
Bibliometrics0.0060.005
Science and technology studies0.0020.001
Scholarly communication0.0040.004
Open science0.0060.005
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0050.002

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 head0.100
GPT teacher head0.362
Teacher spread0.262 · 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
GenreReview

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

Citations17
Published2024
Admission routes1
Has abstractyes

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