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.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".