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Record W3172082710

Dietary Supplements in Adults Taking Cardiovascular Drugs [Internet]

2012· article· en· W3172082710 on OpenAlexaff
Dugald Seely, Salmaan Kanji, Fatemeh Yazdi, Jennifer Tetzlaff, Kavita Singh, Alexander Tsertsvadze, Margaret Sears, Andrea C. Tricco, Teik Chye Ooi, Michèle Turek, Sophia Tsouros, Becky Skidmore, Raymond Daniel, Mohammed Ansari

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicPharmacology and Obesity Treatment
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineObservational studyData extractionMEDLINECochrane LibraryRandomized controlled trialPolypharmacyClinical trialAdverse effectMeta-analysisFamily medicineInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Background A substantial proportion of patients with cardiovascular diseases use dietary supplements in anticipation of benefit. This also poses risks of adverse events from supplement-drug interactions and nonadherence associated with polypharmacy. Objectives For supplements commonly used by patients with cardiovascular disease, we examined benefits, harms, and effects on cardiovascular drug pharmacokinetics of coadministration of dietary supplements with cardiovascular drugs. We also sought evidence regarding variability among subgroups, and of statistical interactions between supplements and drugs. Data Sources We searched MEDLINE®, Embase, the Cochrane Library, International Bibliographic Information on Dietary Supplements (IBIDS), and Allied and Complementary Medicine Database (AMED), as well as gray literature, from inception to September 2011. Study Selection Following a predefined protocol, two reviewers included experimental and observational studies comparing a supplement plus cardiovascular drug versus drug alone published in English or German; other languages were excluded due to concerns with study quality and applicability. Data Extraction One reviewer extracted data into a standardized electronic form, assessed study risk of bias, graded the strength of the body of evidence, and reported its applicability. Study risk of bias and strength of evidence regarding gradable outcomes were independently verified, as was a random 10 percent subset of all data. Data Synthesis Sixty-seven randomized controlled trials, two controlled clinical trials, and one observational study contributed evidence of limited validity in highly selected populations. Evidence was insufficient for all gradable clinical efficacy and harms outcomes (e.g., mortality, thrombotic events, serious adverse events) because there were few, small studies per supplement. One pragmatic trial in women showed no benefit from coadministering vitamin E with aspirin on a composite cardiovascular outcome. Evidence for most intermediate outcomes of efficacy was insufficient or of low strength and suggested no effect. Notable findings were incremental improvement of triglyceridemia with omega-3 fatty acid supplementation, stabilization of international normalized ratio with vitamin K added to warfarin therapy, and improved high-density lipoprotein cholesterol (HDL-C) with added garlic. Clinically nonsignificant or otherwise inconclusive changes were noted for pharmacokinetic outcomes. Limitations The evidence base principally consisted of underpowered short-term studies in selected populations, generally with moderate risk of bias. Conclusions Limitations of the evidence base precluded meaningful conclusions across most supplement-drug combinations. Low-strength evidence indicates benefits of omega-3 fatty acids, vitamin K, and garlic coadministration on specific intermediate outcomes. Evidence regarding harms was inconclusive. Care providers and researchers should query supplement use to improve care and to facilitate research regarding drug-supplement interactions.

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)0.002
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0100.011
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0400.003

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 head0.026
GPT teacher head0.308
Teacher spread0.282 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2012
Admission routes1
Has abstractyes

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