Can Variability in the Hormonal Status of Elderly Women Assist in the Decision to Administer Estrogens?
Bibliographic record
Abstract
Hormone replacement therapy (HRT) has been proposed for the prevention and treatment of many chronic conditions, ranging from osteoporosis, heart disease, urinary incontinence, and Alzheimer's disease. With the exception of osteoporosis, however, many of the suggested benefits remain controversial. Part of the controversy stems from the relative absence of randomized controlled trials, particularly those enrolling sufficient numbers of elderly women. We propose that another factor may also contribute, one that has been overlooked - failure to consider the variable endogenous estrogen status of elderly women. Highly variable levels of estrogens are present in nearly all postmenopausal women, even at advanced ages. Similar to other endocrine systems, estrogen deficiency and the need for its replacement are, therefore, likely to be relative rather than absolute. Recent studies indicate that elderly women who are less able to compensate for declining ovarian 17beta-estradiol production by adipose synthesis of estrone (E1) may be at greater risk for certain chronic conditions associated with relative estrogen deficiency. Because many markers of estrogen deficiency exhibit overlap between risk groups, their clinical usefulness as predictors of frailty, disability, and response to HRT has been limited. Future studies will need to focus not only on the use of highly variable circulating serum estrogen levels but also on markers of overall estrogenic effects at the level of individual target tissues (i.e., markers of bone turnover, karyopyknotic index on a vaginal wall smear). We propose that a clinical approach that takes into consideration the remarkable heterogeneity (physiological as well as psychological) of elderly women will enable us to approach the decision about HRT in a more individualized and possibly better targeted fashion.
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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 | 0.012 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.002 | 0.002 |
| 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".