A review of features and characteristics of smart medication adherence products
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Smart medication adherence products (smart MAPs) capture and transmit real-time medication intake by using various means of connectivity, allowing for remote monitoring. Numerous such products with different features are available to address medication nonadherence. A comparison of the features of these products is needed for clinical decision-making. Therefore, the objective of this review was to compare smart MAPs available for in-home use. Methods: We searched grey and published literature and videos to identify smart MAPs. To be considered smart, products required 2 features: connectivity (the ability for collected data to exist outside the physical device) and automaticity (the ability for data to be analyzed or processed automatically). Products were excluded if product descriptions were not available in English, not for in-home use and unable to dispense medications. Results: Of the 51 products identified, 38 commercially available and 13 prototypes met the definition. Of these, 75% ( n = 38) contained alarms, 24% ( n = 12) were unit-dose, 63% ( n = 32) were multidose, 43% ( n = 22) had locking features, 41% ( n = 21) were portable and 88% ( n = 45) sent notifications to patients. The cost of marketed products, excluding subscriptions, ranged from $10 to $1500 USD. Some products required a monthly ( n = 16) or yearly ( n = 1) subscription ranging from $10 to $100 USD. Discussion: There is a growing market of smart MAPs for in-home patient use with variable features. Clinicians can use these features to identify and recommend products according to the specific needs of their patients to address medication adherence. Can Pharm J (Ott) 2021;154:xx-xx.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it