A randomized controlled trial with a Canadian electronic pill dispenser used to measure and improve medication adherence in patients with schizophrenia
Bibliographic record
Abstract
OBJECTIVE: Medication adherence is extremely important in preventing relapse and lowering symptoms in schizophrenic patients. However, estimates show that nearly half of these patients have poor adherence. The Brief Adherence Rating Scale (BARS) seems to be the most reliable tool assessing adherence in schizophrenia and shows that the antipsychotic adherence ratio (AAR) is about 49.5% in schizophrenia. The aim of the study was to test if an electronic pill dispenser named DoPill(®) improved AAR of schizophrenic patients. Furthermore, we compared AAR obtained by the DoPill(®) and the BARS, in order to verify whether the DoPill(®) provides reliable assessment of medication adherence. METHODS: The DoPill(®) is a smart pill dispenser that beeps and flashes at the appropriate time of the day. Each of its 28 compartments is covered by a plastic lamina that, when taken off, sends a signal to the pharmacist. Patients were randomized to the DoPill(®) or treatment as usual groups for 6 weeks. The BARS was used as a reference measure. RESULTS: Forty-six percent of patients were deemed to be non-adherent with antipsychotic medication. The mean AAR was 67% after 6 weeks. DoPill(®) recorded better AAR than some of those found in the literature and were lower than the BARS estimate we found. CONCLUSION: These results suggest that DoPill(®) is a valid tool that provides more reliable and objective data for the clinician about their patient's adherence, than existing assessment tools like the BARS. Furthermore, the device may help patients successfully manage their medication regimen.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".