Nutrition science mustn't accept a lower level of evidence
Notice bibliographique
Résumé
We read with interest the article “Evidence-based criteria in the nutritional context” by Blumberg et al.,1 who postulated that the level of confidence in nutrition science should be lower than that needed for other branches of medicine, particularly the level of certainty required for drugs. While we agree that drug evidence requires a very high level of rigor, we believe that nutrition science should not and need not be complacent with lower levels of evidence than is required for other branches of medical science. Certainly drug evidence has a high requirement for rigor, for at least three reasons: 1) the involvement of commercial interests, 2) the ability to easily design placebo-controlled randomized controlled drug trials, and 3) the potential for powerful negative effects and side effects. However, all areas of science and medicine (including nutrition science) will always have unknowns. Therefore, we must rely on incomplete knowledge to inform practice. It is of prime importance that we be aware of what knowledge we have and which hypotheses we have not had confirmed, while continuing to strive to improve our understanding of biological systems. Sometimes, relationships that are described in observational studies are not found to be cause-and-effect relationships when randomized control trials are used to test them. Two examples in nutritional science of observed relationships that were not found to be causal include beta-carotene and B vitamins. Both beta-carotene and B vitamins showed promise in observational studies for the control of chronic diseases, i.e., beta-carotene for cancer2 and B vitamins for lowering homocysteine and cardiovascular disease.3 However, large randomized trials did not uphold the results from observational studies. Randomized trials of beta-carotene showed it increased the risk of lung and gastric cancer among smokers,4 and while effective in reducing homocysteine, folic acid had no significant effects on vascular outcomes.5 These examples illustrate how observational study data should not be considered to indicate proof of a relationship, and randomized trials are needed to make causal conclusions. Cause-and-effect relationships cannot be determined from observational data, since the variable being examined as the cause is not isolated from other possible causes. Observational studies are useful for developing hypotheses, which can then be proven or disproven by randomized controlled trials (RCTs), in which the variables are carefully controlled so that causality may be determined. Until we have consistent RCT evidence, the best knowledge we have is the graded body of existing evidence. In other words: “Correlation does not imply causation.” We agree with the authors that “nutrient policy decisions will have to be made using the totality of the available evidence,” as this is the basis for decision making in evidence-informed medicine and epidemiology. However, we disagree that we should accept observational studies as definitive in nutrition science and thus become complacent. There are many examples of RCTs in nutrition science.3,–10 In the field of nutrition, all types of research are needed, i.e., observational studies to document prevalence and generate hypotheses, animal studies to understand biochemical mechanisms, qualitative studies to understand the human condition, and RCTs to establish causal relationships. All areas of science need to consider the whole body of literature through quality appraisal and consideration of the highest quality RCT evidence, when available. The Oxford Centre for Evidence Based Medicine summarizes this concept well: “What are we to do when the irresistible force of the need to offer clinical advice meets with the immovable object of flawed evidence? All we can do is our best: give the advice, but alert the advisees to the flaws in the evidence on which it is based.”11
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».