ArticleCureus2022
Estimating Lean Body Weight in Adults With the PAWPER XL-MAC Tape Using Actual Measured Weight as an Input Variable.
Article in Cureus, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 3 registered trials, which are not on this map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Machine Learning and 3D Image-Based Modeling for Real-Time Body Weight and Body Composition Estimation During Emergency Medical Care. Study 3 - Measure the Accuracy of Weight Estimations by The 3D Camera System in Acutely Ill or Injured Emergency Department Patients and Compare This Accuracy Against That of Standard Care.
Machine Learning and 3D Image-Based Modeling for Real-Time Body Weight and Body Composition Estimation During Emergency Medical Care. Study 1 - Establish a Model Using a Single 3D Camera Image of a Supine Patient to Accurately Estimate TBW, IBW And LBW.
Machine Learning and 3D Image-Based Modeling for Real-Time Body Weight and Body Composition Estimation During Emergency Medical Care: Study 2 - Measure the Accuracy and Speed of Weight Estimations Using a 3D Camera System During Simulated Medical Emergencies
Who cites it
0 citing papers in PubMed, 0 citations in OpenAlex.
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Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction Accurate drug dose calculation in obese patients requires an estimation of lean body weight (LBW) for dosing hydrophilic medications. Inaccurate weight estimates during the management of critically ill obese adults may contribute to inaccurate drug doses and consequential poor outcomes. Existing methods of LBW estimation or measurement may be very difficult or impossible to use during emergency care. A point-of-care model that could provide rapid, accurate estimates of LBW would, therefore, be of significant clinical value. Methods A model was derived based using the adult version of the PAWPER XL-MAC tape. This derived model used recumbent length and measured total body weight (TBW) to estimate LBW. The derived model was used to generate LBW estimations in a random sample from National Health and Nutrition Examination Survey (NHANES) datasets (n=33,215). The benchmark outcome measure was to achieve >95% of LBW estimations within 20% of DXA-measured fat-free mass (P20>95%) and >70% of estimations within 10% of DXA-measured fat-free mass (P10>70%). Results The new model achieved a P20 of 99.7% and a P10 of 86.4% for LBW in the pooled sample and exceeded the minimum accuracy standards. This accuracy was maintained in both sexes, all ages, all ethnic groups, all lengths and in all habitus types. Conclusions The modified PAWPER XL-MAC model, using TBW as an input variable, proved to be an accurate method of LBW estimation. It could potentially have an important role in facilitating emergency drug dose calculations in critically ill or injured obese adult patients.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.