Evidence map›Paper›PMID 36494770›Full record

ArticleBMC neurology2022

Informing the development of an outcome set and banks of items to measure mobility among individuals with acquired brain injury using natural language processing.

Rehab Alhasani, Mathieu Godbout, Audrey Durand, Claudine Auger, Anouk Lamontagne, Sara Ahmed

Registry-linked trialAbstract read
In one paragraph

Article in BMC neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06867081 (Effectiveness of a Customized Digital Platform to Increase Coordination of Care and Uptake of Evidence-based Practices), which is not on this map. Cited by 2 papers.

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

NCT06867081 nanot yet recruitingnot on this mapstarted 2025, after this paper: background citation

Effectiveness of a Customized Digital Platform to Increase Coordination of Care and Uptake of Evidence-based Practices: a Protocol for a Stepped-wedge Cluster Randomized Trial in Quebec

TypeinterventionalSponsorCentre for Interdisciplinary Research in Rehabilitation of Greater MontrealRan2025 to 2029Enrolled962ConditionsAquired Brain Injury, Traumatic Brain Injuries, Stroke, Cardiovascular DiseasesArmsThe intervention will be the BRILLIANT Platform, an online platform which provides five Modules aimed at increasing coordination of care and direct rehabilitation time
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
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

6 authors.

Rehab AlhasaniSchool of Physical and Occupation Therapy, Faculty of Medicine, McGill University, 3655 Sir William-Osler, Montreal, QC, H3G 1Y6, Canada.
Mathieu GodboutUniversité Laval, Laval, Quebec, Canada.
Audrey DurandUniversité Laval, Laval, Quebec, Canada.
Claudine AugerCentre for Interdisciplinary Research in Rehabilitation of Greater Montreal, Montreal, Quebec, Canada.
Anouk LamontagneSchool of Physical and Occupation Therapy, Faculty of Medicine, McGill University, 3655 Sir William-Osler, Montreal, QC, H3G 1Y6, Canada.
Sara AhmedSchool of Physical and Occupation Therapy, Faculty of Medicine, McGill University, 3655 Sir William-Osler, Montreal, QC, H3G 1Y6, Canada. sara.ahmed@mcgill.ca.ORCID http://orcid.org/0000-0001-5172-6790

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe sheer number of measures evaluating mobility and inconsistencies in terminology make it challenging to extract potential core domains and items. Automating a portion of the data synthesis would allow us to cover a much larger volume of studies and databases in a smaller fraction of the time compared to the usual process. Thus, the objective of this study was to identify a comprehensive outcome set and develop preliminary banks of items of mobility among individuals with acquired brain injury (ABI) using Natural Language Processing (NLP).

methodsAn umbrella review of 47 reviews evaluating the content of mobility measures among individuals with ABI was conducted. A search was performed on 5 databases between 2000 and 2020. Two independent reviewers retrieved copies of the measures and extracted mobility domains and items. A pre-trained BERT model (state-of-the-art model for NLP) provided vector representations for each sentence. Using the International Classification of Functioning, Disability, and Health Framework (ICF) ontology as a guide for clustering, a k-means algorithm was used to retrieve clusters of similar sentences from their embeddings. The resulting embedding clusters were evaluated using the Silhouette score and fine-tuned according to expert input.

resultsThe study identified 246 mobility measures, including 474 domains and 2109 items. Encoding the clusters using the ICF ontology and expert knowledge helped in regrouping the items in a way that is more closely related to mobility terminology. Our best results identified banks of items that were used to create a 24 comprehensive outcome sets of mobility, including Upper Extremity Mobility, Emotional Function, Balance, Motor Control, Self-care, Social Life and Relationships, Cognition, Walking, Postural Transition, Recreation, and Leisure Activities, Activities of Daily Living, Physical Functioning, Communication, Work/Study, Climbing, Sensory Functions, General Health, Fatigue, Functional Independence, Pain, Alcohol and Drugs Use, Transportation, Sleeping, and Finances.

conclusionThe banks of items of mobility domains represent a first step toward establishing a comprehensive outcome set and a common language of mobility to develop the ontology. It enables researchers and healthcare professionals to begin exposing the content of mobility measures as a way to assess mobility comprehensively.

Indexed as

Brain InjuriesPersons with DisabilitiesActivities of Daily LivingDisability EvaluationHumansNatural Language ProcessingAcquired brain injuryCore Outcome SetItem banksMachine LearningMobilityNatural Language Processing

Identifiers

PMID36494770
PMCPMC9733317

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Registered trials

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.