Evidence map›Paper›PMID 38724863›Full record

ArticleNeurosurgical review2024

Automated translation accurately translates recorded pediatric neurosurgery clinic conversations between Spanish and English.

Benjamin Succop, Meghan Currin, Gabriella Hesse, Hannah Black, Bethany Andrews, Scott Wentworth Elton, Carolyn Quinsey

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In one paragraph

Article in Neurosurgical review, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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

  1. 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

7 authors.

Benjamin Succop *School of Medicine, University of North Carolina - Chapel Hill, Chapel Hill, NC, USA. benjamin_succop@med.unc.edu.
Meghan Currin *School of Medicine, University of North Carolina - Chapel Hill, Chapel Hill, NC, USA.
Gabriella HesseSchool of Medicine, University of North Carolina - Chapel Hill, Chapel Hill, NC, USA.
Hannah BlackSchool of Medicine, University of North Carolina - Chapel Hill, Chapel Hill, NC, USA.
Bethany AndrewsDepartment of Neurosurgery, University of North Carolina - Chapel Hill, Chapel Hill, NC, USA.
Scott Wentworth EltonDepartment of Neurosurgery, University of North Carolina - Chapel Hill, Chapel Hill, NC, USA.
Carolyn QuinseyDepartment of Neurosurgery, University of North Carolina - Chapel Hill, Chapel Hill, NC, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe purpose of this study is to analyze an automated voice to text translation device by reporting the translation accuracy for recorded pediatric neurosurgery clinic conversations, classifying errors in translation according to their impact on overall understanding, and comparing the incidence of these errors in English to Spanish vs. Spanish to English conversations.

methodsEnglish and Spanish speaking patients at a single academic health system's outpatient pediatric neurosurgery clinic had their conversations recorded. These recordings were played back to a Google Pixel handheld smartphone with Live Translate voice to text translation software. A certified medical interpreter evaluated recordings for incidence of minor errors, errors impacting understanding, and catastrophic errors affecting patient-provider relationship or care. Two proportion t-testing was used to compare these outcomes.

results50 patient visits were recorded: 40 English recordings translated to Spanish and 10 Spanish recordings translated to English. The mean transcript length was 4244 ± 992 words. The overall accuracy was 98.2% ± 0.5%. On average, 46 words were missed in translation (1.09% error rate), 31 understanding-altering translation errors (0.73% error rate), and 0 catastrophic errors were made. There was no significant difference in English to Spanish or vice versa.

conclusionVoice to text translation devices using automatic speech recognition accurately translate recorded clinic conversations between Spanish and English with high accuracy and low incidence of errors impacting medical care or understanding. Further study should investigate additional languages, assess patient preferences and potential concerns with respect to device use, and compare these devices directly to medical interpreters in live clinic settings.

Indexed as

LanguageTranslatingChildFemaleHumansMaleNeurosurgeryPediatricsAccess to CareGlobal neurosurgeryPediatric Neurosurgery ClinicQuality ImprovementTranslation

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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.