Evidence map›Paper›PMID 35915087›Full record

ArticleBDJ open2022

Determining the performance of a temperature sensor embedded into a mouthguard.

Leonardo de Almeida E Bueno, William Milnthorpe, Jeroen H M Bergmann

Open access · goldAbstract read
In one paragraph

Article in BDJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 24% of its field
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, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
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

3 authors at 1 institution in 1 country.

Leonardo de Almeida E BuenoNatural Interaction Lab (NIL), Department of Engineering Science, University of Oxford, Thom Building, Parks Road, Oxford, OX1 3PJ, UK.ORCID http://orcid.org/0000-0002-0347-4581
William MilnthorpeNatural Interaction Lab (NIL), Department of Engineering Science, University of Oxford, Thom Building, Parks Road, Oxford, OX1 3PJ, UK.
Jeroen H M BergmannNatural Interaction Lab (NIL), Department of Engineering Science, University of Oxford, Thom Building, Parks Road, Oxford, OX1 3PJ, UK. Jeroen.Bergmann@eng.ox.ac.uk.ORCID http://orcid.org/0000-0001-7306-2630
University of Oxford · GB

Funding

RCUK | Engineering and Physical Sciences Research Council (EPSRC) EP/R511742/1
6 · The paper itself

Abstract

objectiveThis study aimed to determine the steady-state errors of oral-based temperature sensors, that are embedded in mouthguards, using a robust assessment process. MATERIALS AND

methodsFour electronic boards with temperature sensors were encapsulated in mouthguards made from ethylene-vinyl acetate (EVA). The error and time to reach steady-state temperature were determined using a thermostatic water bath during three different conditions (34, 38.5 and 43 °C). Subsequently, a case study of one volunteer wearing the instrumented mouthguard is presented.

resultsThe water bath tests showed that a mean absolute error of 0.2 °C was reached after a maximum of 690 s across all test conditions. The case study yielded an absolute error was 0.2 °C after 1110 s.

conclusionThese results show that an instrumented mouthguard with temperature sensing capabilities can yield a consistent steady-state error that is close to the clinical requirements across a range of temperatures. However, the time it takes to reach steady-state temperature needs to be considered for these systems to correctly interpret the outcomes.

Identifiers

PMID35915087
PMCPMC9343656
OpenAlexW4289317198

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.