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Record W4392813466 · doi:10.53555/sfs.v10i6.2256

Study In Electrolyte Imbalance In Daibetes Patients

2023· article· en· W4392813466 on OpenAlexvenueno aff
Ali Zayed Al Shehri, Manal ateyan Alharbi, Muteb Khaled Aldhwyan, Hassan Ahmad Hassan Mahzari, Reem Ali Mashhour Alhazmi, Fawaz Ibraheem Otaif

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsnot available
Fundersnot available
KeywordsElectrolyteElectrolyte imbalanceMedicineEnvironmental sciencePsychologyInternal medicineChemistry

Abstract

fetched live from OpenAlex

Electrolyte imbalance is a common complication in patients with diabetes, leading to significant morbidity and mortality. This study aims to investigate the prevalence and impact of electrolyte imbalance in diabetes patients at the Master level. A thorough examination of the literature on this topic was conducted, providing insights into the pathophysiology, risk factors, and management strategies for electrolyte imbalance in diabetes patients. The method involved a systematic review of existing studies, analyzing data on electrolyte levels and clinical outcomes. The results identified a high prevalence of electrolyte disturbances in diabetes patients, particularly hypokalemia and hyperkalemia. The discussion delved into the mechanisms underlying electrolyte disturbances in diabetes, highlighting the importance of early detection and appropriate management. The conclusion emphasized the need for close monitoring of electrolyte levels in diabetes patients and the implementation of targeted interventions to prevent complications.

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)0.003
metaresearch head score (Gemma)0.008
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.155
GPT teacher head0.324
Teacher spread0.169 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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