Med Box: Smart Medication Reminding & Monitoring
Bibliographic record
Abstract
Effective medication management plays a vital role in a patient's recovery, and timely adherence to prescribed medication is key to achieving positive health outcomes. In our fast-paced society, many individuals face challenges in maintaining their medication schedules, which can significantly impact their recovery process. This concern is particularly pronounced among older adults and those managing chronic conditions. Inadequate medication adherence not only threatens health but can also lead to severe complications. Medication management applications have surfaced, although there has been limited advancement in this area in Pakistan, with few hospitals adopting comprehensive medication management systems. There is also a notable gap in home-based solutions that can effectively track medication intake and maintain detailed medication histories. This study aims to evaluate a newly developed medication management system that enhances patient adherence while minimizing medication errors and providing a reliable record of medication history. The proposed prototype includes both a hardware component and a mobile application called "Med App," designed to support patients in managing their medications and documenting their medication journeys. The integrated hardware features five automated containers for medication storage, all operated seamlessly through the Med App. It leverages advanced sensors and image processing technologies to ensure precise medication dispensing and tracking within each container. All collected data is securely stored on the Firebase platform, integrating information from both the app and the hardware.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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) | 0.134 | 0.052 |
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".