Evidence map›Paper›PMID 36869185›Full record

ReviewCurrent neurology and neuroscience reports2023

Advances in the Rehabilitation of Hemispatial Inattention.

Neena R Singh, Alexander P Leff

Full text readReview
In one paragraph

Review in Current neurology and neuroscience reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the 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.

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. Review
  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

2 authors.

Neena R SinghUCL Queen Square institute of Neurology, University College London, London, UK.
Alexander P LeffUCL Queen Square institute of Neurology, University College London, London, UK. a.leff@ucl.ac.uk.ORCID 0000-0002-0831-3541

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewThere continue to be a plethora of approaches to the rehabilitation of hemispatial inattention, from different forms of sensory stimulation (visual, auditory and somatosensory feedback), through all major modes of non-invasive brain stimulation to drug therapies. Here we summarise trials published in the years 2017-2022 and tabulate their effect sizes, with the aim of drawing on common themes that may serve to inform future rehabilitative studies. RECENT

findingsImmersive virtual reality approaches to visual stimulation seem well tolerated, although they have yet to yield any clinically relevant improvements. Dynamic auditory stimulation looks very promising and has high potential for implementation. Robotic interventions are limited by their cost and are perhaps best suited to patients with a co-occurring hemiparesis. Regarding brain stimulation, rTMS continues to demonstrate moderate effects but tDCS studies have yielded disappointing results so far. Drugs, primarily aimed at the dopaminergic system, often demonstrate beneficial effects of a medium size, but as with many of the approaches, it seems difficult to predict responders and non-responders. Our main recommendation is that researchers consider incorporating single-case experimental designs into their studies as rehabilitation trials are likely to remain small in terms of patient numbers, and this is the best way to deal with all the factors that cause large between-subject heterogeneity.

Indexed as

Mental DisordersVirtual Reality Exposure TherapyHumansTranscranial Direct Current StimulationTranscranial Magnetic StimulationBrain stimulationDopaminergic therapyHemispatial inattentionNeglectRehabilitationSensory stimulation

Identifiers

PMID36869185
PMCPMC10011344

What OpenQuestion holds

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measurements read11
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.