Constraints, concerns and considerations about the necessity of estimating free glucocorticoid concentrations for field endocrine studies
Bibliographic record
Abstract
Summary We evaluate the utility of measuring corticosteroid‐binding globulins (CBG) and estimating free glucocorticoid (GC) concentrations for field endocrine studies. We assert that for three general reasons, measurement of free GCs might not be more useful than total GCs for many studies. First, estimates of so‐called ‘free’ GCs are likely inaccurate, in part because of the following: (i) other factors in the blood also bind GCs, (ii) CBG binds plasma steroids other than GCs and (iii) CBG binding affinity can vary with local conditions, such as enzymatic activity and tissue temperature. Second, evidence suggests an active role for CBG‐bound GCs, CBG or both, in the vertebrate stress response, calling into question the validity and generality of the free hormone hypothesis. Third, free and total GCs function over different time frames. Free GCs are likely important in the seconds‐to‐minutes time‐scale of interaction with tissues, but total GCs could function at minutes‐to‐hours time‐scales by serving as the reservoir to continue supplying GCs to tissues. As transcription regulators, most GC effects manifest in hours; thus, total GCs would be the most appropriate measure for estimating total biological impact. Our understanding of the biochemistry and the biological actions of both GCs and CBG indicates that total GC concentrations are currently less prone to error and more biologically interpretable than estimates of free hormone. Although further work is necessary, total GC titres currently remain the most accurate and informative estimates of stress hormone levels to address biological questions in nature.
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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 | 0.109 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".