A systematic review OF splitting a tablet obtain an accurate dose

Niharikareddy Meenigea

Abstract


A systematic review of splitting tablets suggests that the accuracy of split doses can vary depending on various factors, including the tablet characteristics, the splitting method used, and the skills of the individual performing the splitting. However, when done correctly with appropriate guidance and tools, tablet splitting can be an effective way to obtain accurate doses and save costs. It is recommended to consult a healthcare provider or pharmacist for guidance and proper instructions on tablet splitting. Tablet splitting is a widely prevalent practice resulting from the need to alter doses into two or more parts and optimise medicine in individual patients. If a tablet is split unequally problems may arise. The aim of the study was to summarise the literature measuring the effect of tablet splitting on dose accuracy.


References


Bhatia, V., & Bhatia, G. (2013a). Room temperature based fan speed control system using pulse width modulation technique. International Journal of Computer Applications, 81(5).

Bhatia, V., & Whig, P. (2013b). A secured dual tune multi frequency based smart elevator control system. International Journal of Research in Engineering and Advanced Technology, 4(1), 1163–2319.

kolla, V. (2019). Social Distancing detection using machine learning. International Journal of Sustainable Development in Computing Science, 1(1), 21-30. Retrieved from https://www.ijsdcs.com/index.php/ijsdcs/article/view/271

meeeniga, N. (2019). EMOJIFY-CREATE YOUR OWN EMOJIS WITH DEEP LEARNING. International Journal of Sustainable Development in Computing Science, 1(1), 31-39. Retrieved from https://www.ijsdcs.com/index.php/ijsdcs/article/view/272

kolla, V. (2009). LANE DETECTION SYSTEM USING APPLICATION OF MACHINE LEARNING. Transactions on Latest Trends in Health Sector, 1(1). Retrieved from https://www.ijsdcs.com/index.php/TLHS/article/view/270

Whig, P. (2019a). A Novel Multi-Center and Threshold Ternary Pattern. International Journal of Machine Learning for Sustainable Development, 1(2), 1–10.

Whig, P. (2019d). Exploration of Viral Diseases mortality risk using machine learning. International Journal of Machine Learning for Sustainable Development, 1(1), 11–20.

Whig, P., & Ahmad, S. N. (2011a). On the performance of ISFET-based device for water quality monitoring. Int’l J. of Communications, Network and System Sciences, 4(11), 709.

Whig, P., & Ahmad, S. N. (2012a). A CMOS integrated CC-ISFET device for water quality monitoring. International Journal of Computer Science Issues, 9(4), 1694–1814.

Whig, P., & Ahmad, S. N. (2012f). Performance analysis of various readout circuits for monitoring quality of water using analog integrated circuits. International Journal of Intelligent Systems and Applications, 4(11), 103.

Whig, P., & Ahmad, S. N. (2013a). A novel pseudo-PMOS integrated ISFET device for water quality monitoring. Active and Passive Electronic Components, 2013.

Whig, P., & Ahmad, S. N. (2014a). Development of economical ASIC for PCS for water quality monitoring. Journal of Circuits, Systems and Computers, 23(06), 1450079.

Whig, P., & Ahmad, S. N. (2014c). Simulation of linear dynamic macro model of photo catalytic sensor in SPICE. COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 International Journal of Machine Learning for Sustainable Development

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Impact Factor : 

JCR Impact Factor: 5.9 (2020)

JCR Impact Factor: 6.1 (2021)

JCR Impact Factor: 6.7 (2022)

JCR Impact Factor: 7.6 (2023)

JCR Impact Factor: 8.6 (2024)

JCR Impact Factor: Under Evaluation (2025)

A Double-Blind Peer-Reviewed Refereed Journal