Review: antioxidant supplementation does not reduce gastrointestinal cancer
Bibliographic record
Abstract
Bjelakovic G, Nikolova D, Simonetti RG, et al . Antioxidant supplements for prevention of gastrointestinal cancers: a systematic review and meta-analysis. Lancet 2004;364:1219–28. Bjelakovic G, Nikolova D, Simonetti R, et al . Antioxidant supplements for preventing gastrointestinal cancers. Cochrane Database Syst Rev 2004;(4):CD004183. Q Do antioxidant supplements reduce the risk of gastrointestinal cancer? ### ![Graphic][1] Data sources: Cochrane controlled trial registers for 4 gastrointestinal disease groups, Cochrane Central Register of Controlled Trials (2003, Issue 1), Medline (1966 to February 2003), EMBASE/Excerpta Medica (1985 to February 2003), LILACS (1982 to February 2003), Science Citation Index Expanded (1945 to February 2003), Chinese Biomedical Database (1978 to March 2003), reference lists of retrieved studies, and manufacturers of antioxidant supplements. ### ![Graphic][2] Study selection and assessment: randomised controlled trials (RCTs) comparing antioxidant supplementation (β carotene; vitamins A, C, and E; and selenium, separately or in combination) with placebo in patients mainly with non-gastrointestinal … [1]: /embed/inline-graphic-1.gif [2]: /embed/inline-graphic-2.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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".