HIGH COGNITIVE DIETARY RESTRAINT IS ASSOCIATED WITH INCREASED CORTISOL EXCRETION IN POSTMENOPAUSAL WOMEN: RESPONSE TO FENSKE LETTER
Bibliographic record
Abstract
To the Editor: We appreciate Dr. Fenske's interest in our recent report of higher 24-hour urinary cortisol excretion in postmenopausal women with high versus low dietary restraint (1). Dr. Fenske raised two main points in his letter: First, that our results are limited by the lack of specificity of the Bayer ADVIA Centaur method for the measurement of urinary cortisol, and second, that potential differences in the time and volume of urine excretion could account for the difference in cortisol observed between our two groups. While his comments are noteworthy, it is unlikely that they explain the difference in cortisol excretion observed between postmenopausal women with high and low dietary restraint. As indicated by Dr. Fenske, Gray and colleagues showed that the Bayer ADVIA Centaur method of urinary cortisol measurement, like most immunoassay methods, lacks specificity due to its cross-reactivity with cortisol metabolites such as cortisone (2). Given that our study was designed as a between-groups comparison and cortisol excretion was measured by the same method in both groups, this tendency to overestimate cortisol excretion is only pertinent to our results if there might be reason to believe that the compounds with which cross-reactivity occurs could be present in systematically different proportions between the two groups of women we compared.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".