Current psychotropic medication prescribing patterns in late‐life bipolar disorder
Bibliographic record
Abstract
OBJECTIVE: Many patients with bipolar disorder are reaching old age, but whether they are receiving evidence-based psychotropic treatment remains unclear. Our objective was to describe current psychotropic prescribing patterns in a large Canadian late-life bipolar sample. METHODS: Population-based cross-sectional study of 1443 bipolar disorder patients aged ≥ 66, discharged from a psychiatric hospitalization in Ontario, Canada from 1 April 2006 to 31 March 2012. We described psychotropic medication prescribing within 30 days post-discharge. RESULTS: Prescription of ≥2 psychotropic medications was highly prevalent (81.5%). The most common medications were atypical antipsychotics (75.3%), benzodiazepines/zopiclone (42.3%), and antidepressants (38.5%), with less frequent use of valproate (35.4%) and lithium (23.4%). Only 1.4% of patients were on lithium monotherapy, while 4.4% and 15.7% of patients were on antidepressant or atypical antipsychotic monotherapy; 8.9% of all patients were using ≥2 atypical antipsychotics. CONCLUSIONS: In clinical practice, older adults hospitalized with bipolar disorder are often prescribed multiple psychotropic medications upon discharge. In many instances, practices did not reflect bipolar treatment guidelines and may be putting patients at risk for poor physical health and psychiatric outcomes. One such example is the very infrequent use of lithium monotherapy. Future research should examine whether health system-wide protocolized late-life bipolar treatment may optimize prescribing to improve effectiveness and safety. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".