Bibliographic record
Abstract
As illustrated in Figures 6.5 and 6.11, pituitary (LH, FSH and ACTH) and steroid (cortisol) hormone levels in the bloodstream fluctuate dramatically over short periods of time (minutes to hours). In addition, hormones such as GH, ACTH and melatonin show marked circadian variations in their secretion patterns (Figure 6.5). These patterns are physiologically important; for example, we saw in the case of LH secretion, a continuous release, rather than a pulsatile secretion, will not stimulate the ovaries or testes correctly (Figure 7.4). In other words, fertility is dependent on an appropriate pulsatile LH signal reaching the gonads. This principle might be generally applicable to all pituitary hormone secretions. The measurement, or assay, of hormone levels is therefore an important clinical goal, as well as a crucial aid in understanding how hormone levels in blood are regulated and how the neuroendocrine system functions in health and disease. This chapter thus begins with an examination of the methods for measuring hormone levels in the circulation. Analysis of hormone levels The level of a circulating hormone can be measured directly in blood samples or estimated by measuring hormone levels in the saliva, urine or feces , measuring urinary metabolites, or by using bioassays. The determination of glucocorticoids levels in hair , for example, is a way to detect long-term exposure to stress. 8.1.1 Direct measurement of circulating hormones In the past 20 years, there have been striking changes in the analytical techniques used to estimate hormone levels. Until recently, the benchmark in determination of hormone levels was the radioimmunoassay. However, this method, employing antibodies specific to each hormone, and radioactively labeled hormones, is slow, labor-intensive and raised safety problems in the use and disposal of radioactive materials. It is now routine to analyze hormone levels using rapid and automated chemiluminescent or immunometric assays that produce data in a matter of hours, rather than days. A widely used assay is the Enzyme-linked Immunosorbent Assay ( ELISA ).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".