Is there a disparity in osteoporosis referral and treatment among people with affective disorders? A ten-year data linkage study
Bibliographic record
Abstract
AIMS: People with affective disorders (AD) are at increased risk of osteoporosis and fractures. Osteoporosis treatment/referral is thus essential in this population. However, it is unclear whether osteoporosis treatment/referral differs between those with and without AD. This retrospective cohort study compared osteoporosis treatment/referral in people with and without AD across linked primary and mental health care data. METHODS: People with AD (ICD-10 codes F3*) between 1.5.2009-30.11.2019, aged 18+ at first diagnosis, from Lambeth, South London were randomly matched 1:4 to healthy controls based on age band and gender. Outcomes including treatments (prescription of calcium, calcium with vitamin D) and referral (referrals for osteoporosis screening and/or prevention) were analysed using conditional and multivariable logistic regression analyses. RESULTS: People with AD (n = 23,932) were more likely than controls (n = 76,593) to have a recorded prescription of calcium (odds ratio [OR] = 1.64, 95 % confidence interval [CI] 1.40-1.92) and calcium with vitamin D (OR = 2.25, 95 % CI 2.10-2.41), and be referred for osteoporosis screening (OR = 1.87, 95 % CI 1.76-1.99) within 2 years after the date of the first AD diagnosis in adjusted analyses. Older age, female sex, having an ethnic minority background, Class A analgesics use were significant predictors for all osteoporosis management pathways within AD patients. CONCLUSION: Findings from the present study suggest that compared to the general population, people with AD are more likely to receive osteoporosis screening/treatments. Whether this increased screening/treatment is sufficient to reduce the burden of osteoporosis and fractures in this population is unclear and warrants further consideration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".