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Record W2897059753 · doi:10.1161/str.49.suppl_1.wp197

Abstract WP197: Autonomic Nervous System Parameters to Predict the Occurrence of Ischemic Events After Transient Ischemic Attack or Minor Stroke

2018· article· en· W2897059753 on OpenAlexaff
Ling Guan, Jean‐Paul Collet, Garey Mazowita, Victoria E. Claydon, Rollin Brant

Bibliographic record

VenueStroke · 2018
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsBrantford Energy (Canada)Simon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsMedicineHeart rate variabilityInternal medicineStroke (engine)CardiologyLogistic regressionAutonomic nervous systemStressorMorningArea under the curveObservational studyHeart rateBlood pressure

Abstract

fetched live from OpenAlex

Background: Stress response is tightly regulated by the autonomic nervous system (ANS) which can be measured by heart rate variability (HRV). Traditional risk factors and acute triggers for ischemic stroke and transient ischemic attack (TIA) are considered as chronic and acute stressors, respectively. These risk factors all contribute to the recurrent ischemic events and are related to ANS dysfunction. The moderate predictive value of ABCD2 score may be due to assessing a limited number of stressors. Aims, Objectives and Hypotheses: We proposed to 1) assess whether HRV parameters, as markers of ANS function and stress, can predict secondary ischemic events after TIA or minor stroke, and 2) compare the HRV-based predictive tools with ABCD2 score. We expected that using HRV indicators can enhance the prediction of ischemic events. Methods: This is a prospective observational study. Patients developed TIA or minor stroke within 48 hours were eligible. The main study variables included: ABCD2 score, HRV assessment from 24-hour Holter recording, and psychological stress. HRV measurement included calculations of both absolute values and changes of HRV frequency-domain parameters: high frequency (HF), normalized HF, HF +low frequency, and total power. Patients were followed for 90 days to assess the development of outcome events. Logistic regression was employed for data analyses. Area under the curve (AUC) and diagnostic tests were used to assess models’ predictive power. Results: Final analyses include data collected from 201 patients. The most useful HRV predictors were Daytime HF changes (AUC=0.70) and Morning HF value (AUC=0.61). AUCs for the Best Stress Model and the Most Practical Model were 0.82 and 0.76, respectively, which were significantly higher than AUC of ABCD2 score (AUC=0.63), p <0.05. The optimal cut-off points for Daytime HF changes and Morning HF might be increase of 12.5% and 50 ms 2 , respectively. The exploratory models that involved both values and changes of HF had AUCs ≥0.82. Conclusions: Assessing the effects of stress on ANS may be an innovative way to stratify the risk of TIA or minor stroke. Models using HRV parameters, especially HF, provide superior predictive values to ABCD2 score. Future research is needed to validate these results.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.024
GPT teacher head0.275
Teacher spread0.251 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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