798 Hypertensive patients are more at risk of developing osteoporosis and having fractures due to their low bone mineral density
Bibliographic record
Abstract
Introduction: Hypertension and osteoporosis are age-related health risks differentially expressed in men and women. Here we have analyzed their prevalence in a population-based cohort (Cartagene) of Quebec, Canada. This cohort, established in 2010-2011, is composed of 20 000 randomly selected subjects of 40 to 70 years old. The recruitment of the participants was done by the Québec Health Board. Results: Our analysis shows that 32% of the participants are hypertensive, 12.8% osteopenic and 0.6% osteoporotic. We observed that hypertensive participants have a lower bone mineral density than normotensives (p < 0.0001); thus increasing their risk of fractures, particularly in women. However, hypertensive men have also a higher risk of fractures and a lower bone mineral density than normotensive men (p = 0.0260). Moreover, we observed that the interaction between hypertension and the fat free mass was very significant when the bone mineral density was taken into consideration. The risk of fractures increased with a low fat free mass and a BMI < 25 Kg/m2 (p < 0.0001). Conclusion: In conclusion, we observed that not only hypertensive women but also hypertensive men who are usually not diagnosed nor treated for osteoporosis have a lower bone mineral density and more fractures than normotensives. In addition, our results indicate that fat free mass is an important factor for the maintenance of high bone mineral density, in both men and women.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.007 | 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".