Evidence map›Paper›PMID 39006831›Full record

ArticleInterspeech2023

Responsiveness, Sensitivity and Clinical Utility of Timing-Related Speech Biomarkers for Remote Monitoring of ALS Disease Progression.

Hardik Kothare, Michael Neumann, Jackson Liscombe, Jordan Green, Vikram Ramanarayanan

Abstract read
In one paragraph

Article in Interspeech, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Multimodal Technologies for Remote Assessment of Neurological and Mental Health.Journal of speech, language, and hearing research : JSLHR · 2024
    Review
  3. Article
  4. Multimodal Speech Biomarkers for Remote Monitoring of ALS Disease Progression.medRxiv : the preprint server for health sciences · 2024
    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

5 authors.

Hardik KothareModality.AI, Inc., San Francisco, CA, USA.
Michael NeumannModality.AI, Inc., San Francisco, CA, USA.
Jackson LiscombeModality.AI, Inc., San Francisco, CA, USA.
Jordan GreenMGH Institute of Healthcare Professions, Boston, MA USA.
Vikram RamanarayananModality.AI, Inc., San Francisco, CA, USA.

Funding

A digital tool for monitoring speech decline in ALSR42DC019877 · NIDCD · MODALITY.AI , INC · PI GREEN, JORDAN R, RAMANARAYANAN, VIKRAM · 2022 to 2024
$2.3M
NIDCD NIH HHS R42 DC019877
6 · The paper itself

Abstract

In this study, we describe the responsiveness of timing-related measures extracted from read speech in persons with ALS (pALS) collected via a remote patient monitoring platform in an effort to quantify how long it takes to detect a clinically-meaningful change associated with disease progression. We found that the timing alignment of pALS speech relative to a canonical elicitation of the same prompt is the most responsive measure, of the ones considered in this study, at detecting such change in both pALS with bulbar (

Indexed as

amyotrophic lateral sclerosisremote patient monitoringspeech biomarkers

Identifiers

PMID39006831
PMCPMC11246071

What OpenQuestion holds

Textmetadata
LicenceTDM
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.