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Record W3213619984

Co-administration of Complementary Therapies for Cardiovascular Disease Risk Reduction in Type 2 Diabetes

2019· dissertation· en· W3213619984 on OpenAlexaboutno aff
Lucia Andreea Zurbau

Bibliographic record

VenueTSpace · 2019
Typedissertation
Languageen
FieldMedicine
TopicPharmacology and Obesity Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsType 2 diabetesMedicineReduction (mathematics)Administration (probate law)DiseaseDiabetes mellitusInternal medicineIntensive care medicineEndocrinologyPolitical scienceMathematics
DOInot available

Abstract

fetched live from OpenAlex

Cardiovascular disease (CVD) is the main cause of mortality in patients with type 2 diabetes (T2D). Selected dietary and herbal supplements with complementary mechanisms of action have been indicated in management of diabetes along with standard therapy. The objective of this thesis was to determine if a 24-weeks co-administration of 4 diet and herbal supplements will improve CVD risk factors beyond conventional therapy in T2D. The project consisted of a randomized, double-blind, controlled trial with two parallel groups involving 104 individuals with T2D (HbA1c: 70.05%) which were randomly assigned to test (10g viscous fiber, 60g Salba-Chia, 1.5g American and 0.75g Korean red ginseng extracts daily), or energy and fibre matched control (53g oat bran, 25g inulin, 25g maltodextrose and 2.25g wheat bran daily) for 24 weeks. Fasting blood was drawn at weeks 0, 12 and 24. Primary and secondary endpoints was change in HbA1c, blood pressure and serum lipids over 24 weeks. The study was conducted at two centres: St. Michael’s Hospital in Toronto, Canada and Vuk Vrhovac University Clinic in Zagreb, Croatia. Results were computed using an intent-to-treat analysis with multiple imputations. Eighty-seven participants completed the trial (test n=44; control n=43). The test intervention significantly reduced HbA1c (0.270.13% (p=0.03) and 24-hour systolic blood pressure by 3.81.2 mmgHg (p

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.238
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.375
Teacher spread0.347 · 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.

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

Citations0
Published2019
Admission routes1
Has abstractyes

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