Which HIV patients should be screened for osteoporosis
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review provides international insights into the real-world clinical approach to screening for bone mineral density (BMD) and osteoporosis in people living with HIV (PLWH) using opinions from HIV physicians from key regions around the world. RECENT FINDINGS: Although a significant proportion of PLWH are aged over 50, the relative importance of low BMD to clinical care differs significantly between countries and regions, based on factors such as the population at risk, access to adequate screening resources, and physicians' knowledge. Generally, management of osteoporosis in PLWH follows similar principals as for the general population, with risk factors for fracture combined with measurement of BMD by dual energy X-ray absorptiometry in algorithms such as Fracture Risk Assessment Tool, designed to provide an overall risk estimation. Although in most regions age is considered among the most important factors contributing to low BMD and fractures, considerable country and region-specific factors become apparent, such as malnutrition, inactivity and impact of comorbidities, substance abuse, and increasing use of tenofovir disoproxil fumarate. SUMMARY: These opinions highlight the diversity that still exists in the approach to the long-term management of PLWH and highlight challenges facing development of consensus guidelines that can be effectively implemented worldwide.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".