Rethinking of osteoporosis through a sex- and gender-informed approach in the COVID-19 era
Bibliographic record
Abstract
Standards and models of reference for osteoporosis (OP) have been developed for female individuals as they are more likely to be affected by the disease. Nonetheless, OP is also responsible for one-third of hip fractures in male individuals suggesting that a sex-blinded approach to OP may lead to miss opportunities for equity in bone health. OP-related fractures, especially hip fractures, are a matter of immediate concern as they are associated with limited mobility, chronic disability, loss of independence, and reduced quality of life in both sexes. When it comes to sociocultural gender, the effect of gender domains (i.e., identity, roles, relations, and institutionalized gender) on development and management of OP is largely overlooked despite risk factors or protective conditions are gendered. Clinical trials testing the efficacy and safety of anti-OP drugs as well as non-pharmacological interventions have been conducted mainly in female participants, limiting the generalizability of the findings. The present narrative review deals with the sex and gender-based challenges and drawbacks in OP knowledge and translation to clinical practice, also considering the impact of coronavirus disease 2019 pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".