Evaluation of an intensive voice treatment to reduce anterior drooling in children with cerebral palsy: Protocol for a concurrent multiple-baseline, single case experimental design study.

Michelle McInerney, Christine Imms, Paul N Carding, Dinah S Reddihough

Open access · goldAbstract read

In one paragraph

Article in Contemporary clinical trials communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

0.3field-weighted citation impact, top 42% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

4 authors at 4 institutions in 2 countries.

Michelle McInerneyLecturer in Speech Pathology, School of Allied Health, Australian Catholic University, Level 3 TWH, 8-20 Napier Street, North Sydney, NSW, 2060, Australia.

Christine ImmsApex Australia Chair of Neurodevelopment and Disability, Fellow, Occupational Therapy Australia Research Academy, Department of Paediatrics, The University of Melbourne, Australia.

Paul N CardingProfessor of Speech Pathology, Director, Oxford Institute of Nursing, Midwifery and Allied Health Research (INMAHR), Oxford Brookes University, Oxford, UK.

Dinah S ReddihoughThe Royal Children's Hospital Melbourne, Department of Paediatrics, The University of Melbourne, Neurodisability and Rehabilitation, Murdoch Children's Research Institute, Melbourne, Australia.

Australian Catholic University · AUOxford Brookes University · GBRoyal Children's Hospital · AUThe University of Melbourne · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Anterior drooling is common in children with cerebral palsy (CP) and poses significant risks to the child's health. Causes of drooling include oro-motor dysfunction, inefficient swallowing and reduced sensation in the orofacial musculature. Behavioural interventions are frequently recommended to reduce drooling; however, this is in the absence of high-quality research evidence. This paper describes a protocol for evaluating the effectiveness of the Lee Silverman Voice Treatment LOUD (LSVT LOUD®) in reducing drooling; and optimising speech and swallowing in a group of children with CP. A structured and systematic visual analysis supplemented with statistical analyses will be used to analyse the data. The risk of bias in n-of-1 trials (RoBiNT) Scale [1] guided the design and implementation of the study.

Indexed as

Behavioral interventionCerebral palsyChildDrooling

Identifiers

PMID34825105

DOI10.1016/j.conctc.2021.100872

PMCPMC8605272

OpenAlexW3214985403

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

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.