Conflicts of interest in nutritional sciences: The forgotten bias in meta-analysis
Bibliographic record
Abstract
Awareness of conflicts of interest (COI) in medicine began in the 1980s. More recently, the problem has gained notoriety in nutritional sciences. COI with industry could bias study conclusions in the context of research activities and scientific publications on nutritional sciences. The issue of COI in nutritional sciences deserves more attention and requires careful analyses as biased information can negatively impact the development of dietary guidelines and, ultimately, population health. Decision-making is generally based on available, published evidence, but when the results are ambivalent, it is easier to opt for the status quo and ask for more studies. Readers might wonder if research is subsidized by industry as a counterbalancing strategy based on levels of evidence-only to slow down eminent positions and/or legislation on the food sector? How can this problem be overcome without producing paranoia and McCarthyism while trying to be as methodological as possible?
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 | 0.449 | 0.650 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".