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Record W1982100664 · doi:10.1136/ebm1120

Meta analysis suggests that folic acid supplementation does not reduce risk of stroke, but there may be some benefit when given in combination with vitamins B6 and B12 and in primary prevention

2010· letter· en· W1982100664 on OpenAlexaff
Gustavo Saposnik

Bibliographic record

VenueEvidence-Based Medicine · 2010
Typeletter
Languageen
FieldMedicine
TopicFolate and B Vitamins Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineHomocysteineHyperhomocysteinemiaFolic acidStroke (engine)Internal medicineVitamin B12Meta-analysisB vitaminsRandomized controlled trialGastroenterology

Abstract

fetched live from OpenAlex

Commentary on: 1. Lee M, 2. Hong KS, 3. Chang SC, 4. et al . Efficacy of homocysteine-lowering therapy with folic Acid in stroke prevention: a meta-analysis. Stroke 2010;41:1205–12. [OpenUrl][1][Abstract/FREE Full Text][2] Hyperhomocysteinemia has been associated with premature atherosclerosis with an increased risk of cardiovascular events.1,–,3 Folate and cyanocobalamin (vitamin B12) are important regulators of the metabolism of homocysteine, and studies have shown an inverse relationship between levels of these factors and levels of homocysteine in the blood.4 5 On the basis of epidemiological studies, clinicians and scientists expected that homocysteine-lowering therapy (HLT) would reduce the incident risk of cardiovascular diseases (including stroke). As a result, HLT has been tested in several randomised clinical trials.6,–,9 In the present study, Lee and colleagues report the results of a meta-analysis including randomised, controlled trials assessing the efficacy of folic acid supplementation in the prevention of stroke. They used a strict inclusion criteria: (1) randomised, controlled study; (2) comparison of folic acid supplementation (with or without vitamins B6 and B12) with inactive or low-dose control; (3) the duration of the intervention was longer than 6 months; and (4) study reporting on the number of stroke events in both active … [1]: {openurl}?query=rft.jtitle%253DStroke%26rft_id%253Dinfo%253Adoi%252F10.1161%252FSTROKEAHA.109.573410%26rft_id%253Dinfo%253Apmid%252F20413740%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/ijlink?linkType=ABST&journalCode=strokeaha&resid=41/6/1205&atom=%2Febmed%2F15%2F6%2F168.atom

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Editorial
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Meta-analysislow
models splitAgreement compares identical category sets and study designs across arms.

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)0.014
metaresearch head score (Gemma)0.063
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.063
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0110.011
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0040.001
Research integrity0.0090.008
Insufficient payload (model declined to judge)0.0150.002

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 head0.066
GPT teacher head0.329
Teacher spread0.263 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Meta-analysis
Domainnot available
GenreEditorial · Commentary

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

Citations5
Published2010
Admission routes1
Has abstractyes

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