Bibliographic record
Abstract
PURPOSE OF REVIEW: The interaction between fall and fracture risk factors is an area of increasing clinical relevance, but little information is known about the age-specific issues in bone health unique to HIV-infected adults. The present review will focus on what is known about falls and fall risk factors among HIV-infected adults, and then review the association between decreased muscle, increased adiposity, and frailty with both low bone mineral density (BMD) and falls. RECENT FINDINGS: The rate of falls among middle-aged HIV-infected adults is similar to that of HIV-uninfected adults 65 years and older. Many of the clinical factors that contribute to low BMD overlap with risk factors for falls, resulting in a high risk of a serious fall among older adults with the greatest risk for a fracture. Low muscle mass, increased adiposity and metabolic syndrome, physical function impairment and frailty, common among older HIV-infected adults, contribute to an increased risk for low BMD and falls, and subsequently, may increase the risk of fracture among HIV-infected older adults. SUMMARY: Interventions with dual benefit on reducing fall risk and improving BMD are likely to have the greatest impact on fracture prevention in the older, HIV-infected adult.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".