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Record W2142777124 · doi:10.1177/1054773806295240

Medication Use and Symptoms in Individuals With Mitral Valve Prolapse Syndrome

2007· article· en· W2142777124 on OpenAlexaboutno aff
Kristine Anne Scordo

Bibliographic record

VenueClinical Nursing Research · 2007
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsnot available
FundersNational Institute of Nursing Research
KeywordsMitral valve prolapseMedicineInternal medicineCardiologyMitral valvePhysical therapy

Abstract

fetched live from OpenAlex

Mitral valve prolapse (MVP) is a common valvular heart disease associated with a variety of frightening symptoms. Beta-adrenergic blockers along with calcium channel blockers and anxiolytics are widely used to treat symptoms associated with MVPS despite a lack of evidence that supports their efficacy. This study examined the relationship between prescribed medication use and frequency and intensity of MVPS symptoms. A descriptive cross-sectional survey design was used. Descriptive statistics and Cramér's V correlational analysis were used to answer the research questions. Self-completed questionnaires were mailed to 2,282 MVPS individuals older than 21 years of age throughout the United States and Canada previously diagnosed with MVPS. Of the 837 participants, 337 (40%) were taking one or more medications. Although there were significant positive correlations between anxiety and calcium channel blockers, chest pain and digoxin, and mood swings and digoxin, the correlations were very weak.

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)0.003
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.092
GPT teacher head0.518
Teacher spread0.426 · 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

Citations11
Published2007
Admission routes1
Has abstractyes

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