Risk Factors for Fracture in Alcohol-dependent, Pre-menopausal Females
Bibliographic record
Abstract
Aims: Alcoholism is known to be associated with increased risk of fracture. This study aimed to study bone turnover following alcohol detoxification and to investigate lifestyle factors for low bone density that might coexist with alcohol dependency, which might be amenable to modification. Method: Pre-menopausal female participants were recruited from an alcohol-use dependency unit to a cross-sectional study. A lifestyle questionnaire, including alcohol history, smoking, physical activity, dietary calcium intake, falls, and fracture history was completed. Quantitative heel ultrasonography was performed and broadband ultrasound attenuation (BUA), speed of sound (SOS), t score, and z score were recorded. Blood was taken for bone-turnover markers at baseline and day 5 following admission for alcohol withdrawal.Results: The mean age (SD) of alcohol dependent participants was 41.6 (8.3) years, with participants reporting high levels of current cigarette smoking, physical inactivity, and falls. BUA, SOS, t scores, and z scores were lower than the age-matched reference range in alcohol-dependent participants. Levels of type 1 procollagen (P1NP) increased significantly after five days (p < .001). Conclusions: Alcohol-dependent, pre-menopausal individuals have multiple risk factors for fracture, beyond alcohol excess. These should be addressed and targeted as modification may reduce fracture risk, especially given the apparent recovery of bone turnover on the withdrawal of alcohol.
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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 | 0.000 |
| 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) | 0.003 | 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 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".