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Record W2065238164 · doi:10.4236/ijcm.2014.510080

A Cognitive Behavioural Intervention for the Treatment of Vasovagal and Unexplained Syncope

2014· article· en· W2065238164 on OpenAlexafffund
France Bédard, André Marchand, Teresa Kuś, Bernard Thibault, Bianca D’Antono

Bibliographic record

VenueInternational Journal of Clinical Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Syncope and Autonomic Disorders
Canadian institutionsUniversité de MontréalHôpital du Sacré-Cœur de MontréalDouglas Mental Health University InstituteMontreal Heart InstituteUniversité du Québec à Montréal
FundersInstitut de Cardiologie de Montréal
KeywordsVasovagal syncopeSyncope (phonology)PresyncopeIntervention (counseling)DistressMedicineCognitionPsychological interventionPhysical therapyPsychologyPsychiatryClinical psychologyHeart rateCardiologyInternal medicineBlood pressure

Abstract

fetched live from OpenAlex

Vasovagal syncope and unexplained syncope are the most commonly observed types of syncope in outpatient and hospital settings. Medical interventions usually target at the physiological mechanisms responsible for loss of consciousness in an attempt to limit the frequency of recurrences. However, this type of intervention does not take into account the psychological and functional impact of syncope, nor the importance of psychological variables in triggering and maintaining syncope. In the present pilot study, four participants presenting significant psychological distress and recurrent syncope were treated using a multimodal intervention targeting at both the medical and psychological aspects of the problem. Results showed a significant reduction in the frequency of syncope/presyncope, in the level of emotional distress experienced, as well as improved functional status in three of the four participants. Several recommendations are made to medical practitioners and psychologists, in view of identifying patients likely to benefit from this type of intervention or components thereof.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.951
Threshold uncertainty score0.210

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
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 head0.074
GPT teacher head0.433
Teacher spread0.359 · 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 designOther design
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

Citations1
Published2014
Admission routes2
Has abstractyes

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