Response to: Balancing Tradition and Nutrition: Jamu in Menstrual Cycle–Based Dietary Guidance
Bibliographic record
Abstract
We appreciate the interest in our recent systematic review and meta-analysis published in Nutrition Reviews. While we believe open scientific discussion regarding female-specific research is crucial to progress in this field and ensure females are not ignored in biological and physiological research studies, we are hesitant to agree with many of the statements suggested in the Letter to the Editor by Riza Amalia. Our main concern focuses on the suggestion that the findings from our recent meta-analysis suggest that dietary recommendations for women would benefit from being tailored to specific phases of the menstrual cycle, the recommendation that jamu should be included in dietary recommendations, as well as claims regarding changes in appetite during the luteal phase of the menstrual cycle. First, we do not believe that it is appropriate to recommend menstrual cycle–tailored dietary guidelines based on the scientific evidence available. As we describe in our article,1 there is a large degree of heterogeneity in the tools used to measure energy intake as well as the methods used to characterize menstrual cycle phases. All of the studies included in our meta-analysis measured energy intake using self-reported dietary records, which have been reported to be subject to error and bias,2,3 and more valid methods are now available.4,5 Additionally, the methods used to characterize menstrual cycles and identify different phases are deemed insufficient by current methods6 as most rely on a standard 28-day cycle when cycle lengths are now known to vary and 21–35 days is considered normal.7 Therefore, we believe more high-quality evidence is required before menstrual cycle–tailored nutritional guidelines can be considered. These ideas are further supported by a recent narrative review highlighting the same issues with quality in the measurement of energy intake and the identification of menstrual phase.8 We certainly appreciate the interest and passion in this topic but suggest caution, and more quality research is certainly warranted6,7 in diverse groups of females (ie, different ethnicities, ages, hormonal and non–hormonal contraceptive users) before the notion of menstrual cycle–tailored guidelines can be considered.
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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 | 0.010 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.035 | 0.022 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".