A noninvasive measure of negative-feedback strength, approximate entropy, unmasks strong diurnal variations in the regularity of LH secretion
Bibliographic record
Abstract
The secretion of anterior-pituitary hormones is subject to negative feedback. Whether negative feedback evolves dynamically over 24 h is not known. Conventional experimental paradigms to test this concept may induce artifacts due to nonphysiological feedback. These limitations might be overcome by a noninvasive methodology to quantify negative feedback continuously over 24 h without disrupting the axis. The present study exploits a recently validated model-free regularity statistic, approximate entropy (ApEn), which monitors feedback changes with high sensitivity and specificity (both >90%; Pincus SM, Hartman ML, Roelfsema F, Thorner MO, Veldhuis JD. Am J Physiol Endocrinol Metab 273: E948-E957, 1999). A time-incremented moving window of ApEn was applied to LH time series obtained by intensive (10-min) blood sampling for four consecutive days (577 successive measurements) in each of eight healthy men. Analyses unveiled marked 24-h variations in ApEn with daily maxima (lowest feedback) at 1100 +/- 1.7 h (mean +/- SE) and minima (highest feedback) at 0430 +/- 1.9 h. The mean difference between maximal and minimal 24-h LH ApEn was 0.348 +/- 0.018, which differed by P < 0.001 from all three of randomly shuffled versions of the same LH time series, simulated pulsatile data and assay noise. Analyses artificially limited to 24-h rather than 96-h data yielded reproducibility coefficients of 3.7-9.0% for ApEn maxima and minima. In conclusion, a feedback-sensitive regularity statistic unmasks strong and consistent 24-h rhythmicity of the orderliness of unperturbed pituitary-hormone secretion. These outcomes suggest that ApEn may have general utility in probing dynamic mechanisms mediating feedback in other endocrine systems.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".