IMPROVING THE ACCURACY OF CALCULATION WEIGHTS BY VISUAL INPUT: A PILOT STUDY IN THE EMERGENCY DEPARTMENT OF A LOW MIDDLE-INCOME COUNTRY

Abstract

Accurate weight estimation is critical in emergency medical scenarios requiring immediate interventions. This pilot study explores the feasibility of improving weight calculation accuracy through visual input, focusing on height-based estimations. The research aims to contribute valuable insights to weight estimation methodologies, particularly in resource-constrained settings. The objective of the pilot study is to explore the feasibility of improving weight calculation accuracy through visual input, focusing on height-based estimations. Data was collected encompassing diverse height ranges from 1.45 to 1.94 meters. Comprehensive datasets included actual body weights, estimated weights, standard deviation, and standard error. eBW(kg) = (N− 1)100 is the estimated body weight method, where "N" is the height measured in meters. Body weight classifications were employed to analyze the accuracy of estimations further. Correction factors for everyone were computed. The correction factor separates the obtained data into underweight, close to actual weight, and overweight. Following optimization, the average correction factor for every category is updated. These updated correction factors improve weight estimation precision. Linear regression analyses were conducted to compare actual and estimated weights, visually representing the discrepancies. The calculated correction factors are essential to improving weight calculations in medicine. The thorough research and improvement procedure resulted in revised correction factors significantly improving the precision of weight estimates in the medical field. We can say that the following equation provides a more accurate weight estimate. Wt corrected = (N-1) x 100 x Correction Factor. This revised weight is an enhanced estimate that considers the updated correction factors. The calculated correction variables are essential to improving weight estimations in medicine. The thorough research and improvement procedure resulted in revised correction factors that significantly improved the precision of weight estimations in the medical field. The derived correction factors demonstrate their effectiveness in enhancing weight estimations, notably in specific patient groups. The investigation classifies patients and delivers precise modifications to improve weight estimations, ensuring safer prescribing procedures. The proposed correction variables will be critical in evaluating emergency medicine doses for individuals.