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Record W3119062264 · doi:10.1097/md.0000000000024208

Impact of lifestyle education for type 2 diabetes mellitus

2021· article· en· W3119062264 on OpenAlexaff
Jingjing Zhu, Min Chen, Yuzi Pang, Li Shu-Min

Bibliographic record

VenueMedicine · 2021
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsImpact
FundersNational Natural Science Foundation of China
KeywordsMicroalbuminuriaMedicineInformed consentType 2 Diabetes MellitusDiabetes mellitusPhysical therapyPhysical examinationClinical trialFamily medicineInternal medicineAlternative medicineEndocrinologyPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: To explore the influence of the education of lifestyle in the type 2 diabetes mellitus (T2DM) patients with microalbuminuria as a part of the enhanced multifactorial intervention. METHODS: This study will be conducted from May 2021 to August 2022 at Ningbo No.6 hospital. The experiment was granted through the Research Ethics Committee of Ningbo No.6 hospital (539D035). The patients will be included if they are between 18 and 65 years old and are diagnosed with T2DM with microalbuminuria and the patients who have signed the written informed consent. While the patients will be excluded if they have serious physical comorbidities and patients who are unwilling to offer the informed consent to take part in this experiment. We measure the clinical examination (fasting blood-glucose, glycosylated hemoglobin and routine urine test) timely. Detail of daily dietary intake and lifestyle factors are also recorded. RESULTS: Table 1 reflects the comparison of the biochemical and clinical variables and the lifestyle factors. CONCLUSION: Lifestyle education is effective in facilitating the control of T2DM and reducing microalbuminuria. TRIAL REGISTRATION NUMBER: researchregistry6348.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.028
GPT teacher head0.358
Teacher spread0.330 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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