Comorbidity of chronic disease and potential treatment conflicts in older people dispensed antidepressants
Bibliographic record
Abstract
OBJECTIVES: the study aimed to examine the prevalence of comorbidity, the prescribing of potentially inappropriate medications and treatment conflicts in a large sample of older people who have been dispensed an antidepressant medicine. METHODS: a cross-sectional study of administrative claims data from the Department of Veterans' Affairs, Australia, 1 April-31 July 2007, of veterans aged > or =65 years was conducted. Comorbidities determined using the pharmaceutical-based comorbidity index, Rx-Risk-V. Concomitant medicines that may be potentially inappropriate for patients with depression and areas of treatment conflicts were determined from Australian clinical guidelines or reference compendia. RESULTS: a total of 39,695 subjects were included, with a median of 5 comorbid conditions (inter-quartile range 3-6). Ninety percent of medicine use was attributed to the treatment of comorbid conditions. Eighty-seven percent of the study cohort was identified as having at least one comorbid condition that may cause a potential treatment conflict when an antidepressant is used. Those conditions of most concern included cardiovascular diseases, anxiety disorders, arthritis or pain management and osteoporosis. CONCLUSION: we observed a high level of potentially inappropriate prescribing and treatment conflicts that may arise when caring for older patients dispensed an antidepressant with comorbidity. These have the potential to place a large number of older people with depression at increased risk for adverse events.
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 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) | 0.000 | 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) | 0.000 | 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".