Evidence map›Paper›PMID 24718712›Full record

ArticleDrug safety2014

A comparison of the effects of different typographical methods on the recognizability of printed drug names.

Calvin K L Or, Hailiang Wang

Abstract read
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In one paragraph

Article in Drug safety, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 2 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

4 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. A systematic literature review of LASA error interventions.British journal of clinical pharmacology · 2021
    Pooled it
  2. Pooled it
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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.

Calvin K L OrDepartment of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam, Hong Kong, klor@hku.hk.
Hailiang Wang

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTall Man lettering is a text enhancement method currently recommended by various organizations for distinguishing look-alike drug names to prevent medication errors. However, the literature has suggested that other typographic styles may be more effective.

objectiveOur objective was to examine the effects of text enhancements and orthographic similarity on the accuracy of look-alike drug name differentiation.

methodsWe conducted two experiments that were based on a two-way, repeated-measures design with text enhancement (Tall Man, boldface, boldface plus Tall Man, colored text [red] and contrast with lowercase as a 'no text enhancement' control) and orthographic similarity (low, medium, and high) as factors. Engineering students without a pharmacy background in experiment 1 (n = 40) and student pharmacists in experiment 2 (n = 40) participated in a computer-based drug name differentiation task in which they determined whether the two drug names in each of the 336 name pairs were the same or different. Only the data generated from the pairs in which the two names were different were used in the analysis. The differentiation accuracy was measured as the proportion of correct responses.

resultsIn both experiments, all five text enhancements significantly improved accuracy compared with the lowercase condition; and boldface plus Tall Man and color significantly outperformed Tall Man lettering. A high degree of orthographic similarity yielded the least accuracy, followed by medium and low degrees.

conclusionsHighlighting the differing portions of confusing drug name pairs using enhanced text clearly renders differentiation easier, although Tall Man lettering may not be the most effective choice for this method.

Indexed as

AdultComputersDrug LabelingFemaleHumansMaleMedication ErrorsYoung Adult

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Registered trials

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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.