Sex and Age Differences in Geriatric Pharmacotherapy
Bibliographic record
Abstract
Older men and women are major users of therapeutic drugs.Indeed, modern medicine, including use of therapeutic drugs, has made a major contribution to the aging of our population.There are sex differences in how the body handles a drug (pharmacokinetics) and in how a drug affects the body (pharmacodynamics).These result in sex differences in the effectiveness and safety of drugs.Sex differences are one of many causes of variability in drug response.When predicting drug effects in older adults, sex (biological) must be considered along with gender (sociocultural), age, genetics, disease, other drugs, and sociodemographic factors (Fujita et al., 2023).It is particularly important to consider the intersection between sex and age effects.Until recently, there has been very little consideration of the impact of sex differences in research on drugs for older adults.Most preclinical research was performed in cell cultures without considering sex and in male animal models.Clinical research has disproportionately recruited males over females.Consequently, there are limited data on sex differences.Furthermore, there are limited data from older animals and participants in clinical trials are, on average, much younger than real-life users of drugs.There is a need for better data on the impact of sex and age and their intersections on drug effects.Careful evaluation and consideration of sex differences throughout the drug development and use cycle will improve outcomes of therapeutic drugs for older adults.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 teacher head, 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".