Lowering homocysteine with folic acid and B vitamins did not prevent vascular events in vascular disease
Bibliographic record
Abstract
Lonn E, Yusuf S, Arnold MJ, et al. Homocysteine lowering with folic acid and B vitamins in vascular disease. N Engl J Med 2006;354:1567–77. [OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q In patients with vascular disease, does lowering plasma homocysteine concentrations with folic acid and B vitamins reduce risk of major vascular events? Clinical impact ratings GP/FP/Primary care ★★★★★★☆ IM/Ambulatory care ★★★★★★☆ Neurology ★★★★★★☆ Cardiology ★★★★★★☆ Endocrine ★★★★★☆☆ ### ![Graphic][5] Design randomised placebo controlled trial (Heart Outcomes Prevention Evaluation [HOPE] 2 trial). ### ![Graphic][6] Allocation concealed.* ### ![Graphic][7] Blinding blinded {clinicians, patients, data collectors, outcome assessors, and data analysts}†.* ### ![Graphic][8] Follow up period mean 5 years. ### ![Graphic][9] Setting 145 centres in 13 countries (Canada, USA, Brazil, western Europe, and Slovakia). ### ![Graphic][10] Patients 5522 patients ⩾55 years of age (mean age 69 y, 72% men) with a history of coronary artery, cerebrovascular, or peripheral artery disease, or diabetes with ⩾1 risk factor for atherosclerosis. Exclusion criteria included planned revascularisation and other types of significant cardiovascular disease (CVD). ### ![Graphic][11] Intervention a combined pill with 2.5 mg folic acid, 50 mg vitamin B6, and 1 mg vitamin B12 taken once daily (n = 2758) or placebo (n = 2764). ### ![Graphic][12] Outcomes a composite end point of MI, stroke, or death from CV causes. Secondary outcomes included total ischaemic events (primary outcome components plus hospital admission for unstable angina or revascularisation), death from any cause, hospital admission for unstable angina, and revascularisation. ### ![Graphic][13] Patient follow up 99% (intention to treat analysis). … [1]: {openurl}?query=rft.jtitle%253DN%2BEngl%2BJ%2BMed%26rft_id%253Dinfo%253Adoi%252F10.1056%252FNEJMoa060900%26rft_id%253Dinfo%253Apmid%252F16531613%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/external-ref?access_num=10.1056/NEJMoa060900&link_type=DOI [3]: /lookup/external-ref?access_num=16531613&link_type=MED&atom=%2Febmed%2F11%2F4%2F104.atom [4]: /lookup/external-ref?access_num=000236736900005&link_type=ISI [5]: /embed/inline-graphic-1.gif [6]: /embed/inline-graphic-2.gif [7]: /embed/inline-graphic-3.gif [8]: /embed/inline-graphic-4.gif [9]: /embed/inline-graphic-5.gif [10]: /embed/inline-graphic-6.gif [11]: /embed/inline-graphic-7.gif [12]: /embed/inline-graphic-8.gif [13]: /embed/inline-graphic-9.gif
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 arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".