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Record W2064798204 · doi:10.1016/s0924-9338(10)71139-1

P03-29 - Reduced Cardio-Respiratory Coupling Indicates Suppression of Vagal Activity in Healthy Relatives of Patients with Schizophrenia

2010· article· en· W2064798204 on OpenAlexaff
Sandy Berger, Michael Karl Boettger, Vikram K. Yeragani, Manuel E. Tancer, Karl‐Jürgen Bär

Bibliographic record

VenueEuropean Psychiatry · 2010
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)Vagal toneHeart rate variabilityHeart rateAutonomic nervous systemCardiologyInternal medicineAutonomic functionBreathingRespiratory systemMedicineVentilation (architecture)PsychologyAnesthesiaBlood pressurePsychiatry

Abstract

fetched live from OpenAlex

Previous studies have observed reduced vagal modulation in patients with acute schizophrenia and their first degree relatives, thus suggesting a genetic predisposition. To investigate vagal modulation at brain stem level, we investigated the coupling between heart rate and breathing as a putative measure of central autonomic function in 19 patients, 19 of their relatives and 19 matched control subjects. The interaction of heart rate and breathing was investigated in all groups applying the non-linear parameter cross-ApEn, indicating the asynchrony between both time series. The main finding of our study is a significantly increased cross-ApEn value, indicating reduced central vagal modulation both in relatives and patients suffering from schizophrenia. Our results suggest that autonomic dysfunction in schizophrenia is present in first-degree relatives not only at the target organs as shown previously, but also affects the central vagal component.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.252
Teacher spread0.242 · 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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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