A Novel Text-Message Reminder System to Address Medication Non-Adherence
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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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".