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Record W2014332957 · doi:10.2337/diacare.27.3.839

French Maritime Pine Bark Extract Pycnogenol Dose-Dependently Lowers Glucose in Type 2 Diabetic Patients

2004· letter· en· W2014332957 on OpenAlexfundno aff
Ximing Liu, Ha-Jun Zhou, Peter Rohdewald

Bibliographic record

VenueDiabetes Care · 2004
Typeletter
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersCanadian Diabetes Association
KeywordsMedicineBark (sound)Traditional medicineType 2 diabetesDiabetes mellitusPhytotherapyInternal medicinePharmacologyEndocrinologyAlternative medicineForestry

Abstract

fetched live from OpenAlex

Pycnogenol, a standardized extract from the bark of the French maritime pine, consists of phenolic compounds including catechin, taxifolin, procyanidins, and phenolic acids (1). We investigated whether Pycnogenol has a glucose-lowering effect because of personal verbal communication from patients reporting no need for insulin following supplementation with Pycnogenol. The study was designed as an open, controlled, dose-finding study and was approved by the ethical committee of Guangnamen Hospital. Patients gave written informed consent. We recruited 18 men and 12 women among outpatients of the Guangnamen Hospital and Municipal Dental Hospital. Patients were 28–64 years of age and had a BMI 22–34 kg/m2. Patients with type 2 diabetes were included with fasting plasma glucose between 7 and …

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 categoriesMeta-epidemiology (narrow), 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 score0.184
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.014
GPT teacher head0.224
Teacher spread0.210 · 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

Citations67
Published2004
Admission routes1
Has abstractyes

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