Dietary interventions, statistical power, and unanswered questions
Notice bibliographique
Résumé
We thank Wen et al. for their thoughtful correspondence and their interest in our study (1). It was not feasible for us to measure behavioral factors or assess risk of bias given the comprehensive scoping nature of our review. We agree that behavioral factors and risk of bias are important when evaluating the results of trials, but given that the majority of trials did not include a blinding procedure and that the sample sizes were small, it is likely that bias would have been substantially high if formally evaluated, further limiting confidence in dietary intervention to alter the natural history of cancer. Our study did not assess the role of nutrition in cancer prevention for the general population, and we continue to encourage healthy lifestyle habits and the avoidance of obesity in such a population. Our study focuses on the question of diet in individuals who already have cancer. Although we continue to encourage healthy lifestyles for such patients, we acknowledge with humility that evidence that a diet will change their cancer trajectory is lacking. Although Wen et al. argue that an adequately powered randomized trial may reveal improved cancer outcomes, we bring forward the examples of numerous well-powered trials that showed no effect of specific dietary interventions on cancer outcomes. In breast cancer, randomized trials testing a diet high in fruits and vegetables (n = 3088) and the Mediterranean diet (n = 1542) did not demonstrate a reduction in cancer recurrence (2,3). In addition, in prostate cancer, a trial evaluating a diet high in fruits and vegetables (n = 478) did not change the time to disease progression (4). If a study requires an astronomically large sample size to show a nominal difference in an intervention’s outcome, then by definition, that intervention had a marginal effect. If diet truly is a powerful factor in determining the course of cancer, it should not require a large study to demonstrate that effect. Finally, we agree that randomized trials testing dietary interventions could shift focus away from adherence and feasibility endpoints and test endpoints that are meaningful to patients. We hope that future grant proposals will learn from our work in the design of their clinical trials. There may be a specific diet for a specific cancer that in the future may alter the natural history of the disease in a well-done trial, but our review has not shown such a trial to date. Therefore, we maintain that currently, there is limited evidence to support dietary intervention as a therapeutic tool in cancer and that more rigorous research in this area is needed. No new data were generated for this correspondence. Calvin Smith, BS (Writing—original draft), Chris Booth, MD (Conceptualization; Writing—review & editing), Ghulam Rehman Mohyuddin, MD (Conceptualization; Writing—original draft; Writing—review & editing). No funding was used for this study. Dr Ghulam Rehman Mohyuddin: Royalties for writing from MashupMD, and his site has received funding because he is a site principal investigator. Not applicable.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,368 | 0,758 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,002 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,027 |
| Communication savante | 0,010 | 0,011 |
| Science ouverte | 0,008 | 0,005 |
| Intégrité de la recherche | 0,023 | 0,021 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,002 |
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; l’étiquette directe de Gemma et le classifieur distillé Codex 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 ».