Dietary interventions, statistical power, and unanswered questions
Bibliographic record
Abstract
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.
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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 | 0.368 | 0.758 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.023 | 0.021 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".