Cardiovascular disease treatment among severe mental illness patients: a data linkage study
Bibliographic record
Abstract
Background. Sub-optimal treatment of cardiovascular diseases (CVD) among severe mental illness (SMI) patients may contribute to physical health disparities. Aims. To identify SMI characteristics associated with meeting CVD treatment guidelines.Design & setting. Population-based electronic health record database linkage between primary care and the sole provider of secondary mental health care services in South East London, UKMethods. Cardiovascular disease prevalence, risk factor recording and Quality and Outcomes Framework (QOF) clinical target achievement was compared among 4,056 SMI primary care patients whose records were linked to secondary health care records and 270,669 patients without SMI who were not known to secondary care psychiatric services using multivariate logistic regression modelling. Data available from secondary care records were then used to identify SMI characteristics associated with QOF clinical target achievement.Results. SMI patients with coronary heart disease and heart failure experienced reduced prescribing of betablocker and Angiotensin-Converting Enzyme Inhibitor/Angiotensin Receptor Blockers (ACEI/ARB). A diagnosis of schizophrenia, being identified with any indicator of risk or illness severity, and being prescribed with depot injectable antipsychotic medication was associated with the lowest likelihood of prescribing. Conclusions. Linking primary and secondary care data allows the identification of SMI patients most at risk of under treatment for physical health problems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".