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 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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".