Evidence map›Paper›PMID 31011391›Full record

ArticleThe Pan African medical journal2018

An algorithm to improve the accuracy of emergency weight estimation in obese children.

Mike Wells, Lara Nicole Goldstein

Abstract read
In one paragraph

Article in The Pan African medical journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  6. Validation of two pediatric resuscitation tapes.Journal of the American College of Emergency Physicians open · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Mike WellsDivision of Emergency Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Lara Nicole GoldsteinDivision of Emergency Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionDuring medical emergencies in children, accurate and appropriate weight estimations may ultimately influence the outcome by facilitating the delivery of safe and effective doses of medications. Children at the extremes of habitus, especially obese children, are more at risk of an inaccurate weight estimation and therefore may be more at risk of medication errors. The objective was therefore to develop an algorithm to guide accurate emergency weight estimation in obese children.

methodsRelevant medical evidence was reviewed regarding weight estimation and its role and timing in the resuscitation of obese children. This was used as the basis for a weight-estimation algorithm.

resultsThere was limited evidence regarding the way the weight-estimation systems should be used in obese children other than that the dual length- and habitus-based systems were the most accurate. The methods included in the algorithm were the Broselow tape, the Mercy method, parental estimates, the paediatric advanced weight prediction in the emergency room/ eXtra Length-eXtra Large (PAWPER XL) tape and the Traub-Johnson formula. The algorithm recognised several ways in which weight estimation could be tailored to the clinical scenario to estimate both ideal and total body weight.

conclusionWeight-estimation in obese children must be conducted appropriately to avoid medication errors. This algorithm provides a framework to achieve this.

Indexed as

AlgorithmsBody WeightChildEmergenciesEmergency Service, HospitalHumansMedication ErrorsPediatric ObesityReproducibility of ResultsResuscitationBody weightpediatric obesityresuscitation

Identifiers

PMID31011391
PMCPMC6461967

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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.