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Record W2125124089 · doi:10.6000/1927-5129.2014.10.29

Autonomic Dysfunction in Asian Indian T2DM Patients is Related to Body Fat Content Instead of Insulin Resistance: A DEXA Study

2014· article· en· W2125124089 on OpenAlexvenueno aff
Poonam Punjabi, Prashant Mathur, R.C. Gupta, Itisha Mathur, Jyoti Thanvi, Deepak Gupta, Sandeep Kumar Mathur

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2014
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsnot available
Fundersnot available
KeywordsHeart rate variabilityInsulin resistanceMedicineInternal medicineCardiologyEndocrinologyPlethysmographBody mass indexType 2 diabetesBioelectrical impedance analysisDiabetes mellitusObesityHeart rateBlood pressure

Abstract

fetched live from OpenAlex

Aim: To study autonomic dysfunction in Asian Indian T2DM patients by heart rate variability and it's relation with body fat content, distribution and insulin resistance.Subjects and Methods: Subjects: 33 T2DM patients aged (46.96 ± 8.90 yrs), and 33 healthy controls aged (44.08 ± 9.15 yrs).Methods: Short-term heart rate variability (HRV) was measured by impedance plethysmograph recording of pulse wave in distal superficial arteries. Time domain and Frequency domain analysis of HRV was carried out. Time domain parameters (SDNN, rMSSD, pNN50) and frequency domain parameters (Total Power, LF power, HF Power, LF (nu), HF (nu), LF/HF Ratio) were determined. Body fat content and distribution was estimated by (DEXA). Insulin Resistance was assessed by HOMA-R. Student t test was used for comparison of parameters in two groups. Multiple regression was used to find out relation between parameters of adiposity and HRV.Results: Parameters rMSSD, pNN50, Total power, LF Power, HF Power were significantly lower in diabetics. Total power showed negative correlation with BMI and truncal fat (r=-.43; 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)0.003
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 score0.338
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.018
GPT teacher head0.257
Teacher spread0.239 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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