Review: multivitamins and mineral supplements do not reduce infections in elderly people
Bibliographic record
Abstract
El-Kadiki A, Sutton AJ. Role of multivitamins and mineral supplements in preventing infections in elderly people: systematic review and meta-analysis of randomised controlled trials. BMJ 2005;330:871.[OpenUrl][1][Abstract/FREE Full Text][2] Q In elderly people, do multivitamins and mineral supplements reduce the risk of infections more than placebo? ### ![Graphic][3] Data sources: AMED, Biological Abstracts, British Nursing Index, CINAHL, Science and Social Science Citation Indexes, Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, EBM reviews, EMBASE/Excerpta Medica, IBIDS, Medline, NHS Centre for Reviews and Dissemination databases, and PreMedline (1966 to January 2004); searches of published reviews, guidelines, Health Evidence Bulletin Wales , and conference abstracts; and reference lists of relevant articles. ### ![Graphic][4] Study selection and assessment: randomised controlled trials (RCTs) that compared the effects of a combination of multivitamins and mineral supplements with placebo in elderly people and reported infection related outcomes. Study quality was assessed using the 5 point Jadad scale. ### ![Graphic][5] Outcomes: days with infection, incidence of ⩾1 infection during the study period, … [1]: {openurl}?query=rft.jtitle%253DBMJ%26rft_id%253Dinfo%253Adoi%252F10.1136%252Fbmj.38399.495648.8F%26rft_id%253Dinfo%253Apmid%252F15805125%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=bmj&resid=330/7496/871&atom=%2Febnurs%2F9%2F1%2F24.atom [3]: /embed/inline-graphic-1.gif [4]: /embed/inline-graphic-2.gif [5]: /embed/inline-graphic-3.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
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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".