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Record W2419680091 · doi:10.4172/2368-0512.1000062

Geometric indexes of heart rate of variability identifies autonomic alterations in young patients with type 1 diabetes mellitus

2016· article· en· W2419680091 on OpenAlexvenueno aff
Naiara Maria de Souza, Calos Marcelo Pastre

Bibliographic record

VenueCurrent research. Cardiology · 2016
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsMedicineCardiologyHeart rate variabilityInternal medicineDiabetes mellitusType 2 Diabetes MellitusHeart rateEndocrinologyBlood pressure

Abstract

fetched live from OpenAlex

To compare autonomic modulation in young patients with type 1 diabetes mellitus (DM1) (n=20) and healthy control subjects (n=23) using geometric indexes of heart rate variability (HRV) associated with linear domains of time and frequency; and to verify correlations between geometric indexes and those obtained by the time and the frequency domain of diabetic individuals. MethOdS: Heart rate was recorded as beat-to-beat with subjects supine for 30 min. The following indexes were calculated: RRtri, TINN, Poincar plot, SD1, SD2, SD1/SD2, RMSSD, SDNN, LF and HF (ms/un) and LF/HF. Visual analysis of the Poincar plot was also performed. Independent Student's t test or Mann-Whitney test, and Spearman's correlation test or Pearson's correlation test were used for statistical analysis . ReSultS: Significantly lower values of SD1, SD2, RRtri, RMSSD, SDNN, and LF and HF in ms were observed in patients with DM1 compared with healthy subjects. CONCluSiON: The Poincar plot showed less dispersion of RR intervals for the DM1 group. Strong correlations were observed with the geometric indexes with the time domain and frequency indexes.

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)0.005
metaresearch head score (Gemma)0.003
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 score0.022
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.039
GPT teacher head0.331
Teacher spread0.292 · 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
Published2016
Admission routes1
Has abstractyes

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