A Novel Text-Message Reminder System to Address Medication Non-Adherence
Why this work is in the frame
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Bibliographic record
Abstract
There is a high rate of medication non-adherence, which can lead to disease progression, disability andmortality. This study tested a novel computer-based text message reminder system to improve adherence to medications. This system proved effective in improving adherence to a placebo in healthy volunteers, and to medications in cardiac patients over a two month period. It was especially effective in individuals at prospectively identified to be at high risk of non-adherence. This system represents a simple and scalable method to improve adherence to medications at a clinical or pharmaceutical level. Further research into the impact of repeated reminders is necessary to explore the impacts of text message reminders in other populations and in other lifestyle interventions.Il y a un taux élevé de non-adhérence aux médicaments, ce qui peut provoquer la progression de la maladie, des handicaps et même la mortalité. Cette étude a vérifier un système de rappel fonctionnant par messagerie texte afin d’améliorer l’adhérence aux médicaments. Ce système a été prouvé efficace dans l’amélioration de l’adhérence à un placébo chez des sujets bénévoles, et à des médicaments chez des patients cardiaques, sur une période de 2 mois. Il était particulièrement efficace chez es individus identifiés comme ayant un risqué élevé de non-adhérence. Ce système représente une méthode simple et mesurable utilisée pour améliorer l’adhérence aux médicaments à un niveau pharmaceutique ou clinique. Des recherches plus poussées sur les conséquences de ces rappels répétés sont nécessaires afin d’explorer les impacts des rappels texte chez d’autres populations et dans d’autres interventions concernant les habitudes de vie.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it