Evidence map›Paper›PMID 41993838›Full record

ArticleFrontiers in psychiatry2026

Subtle impairments of facial emotion expressions in individuals at ultra-high risk for psychosis.

Tina D Kristensen, Bjørn H Ebdrup, Karen S Ambrosen, Merete Nordentoft, Louise B Glenthøj, Stella Grasshof

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 2026. 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
–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

0 citing papers in PubMed.

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

6 authors.

Tina D KristensenCenter for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Center Glostrup, Copenhagen University Hospital - Amager and Hvidovre, Hvidovre, Denmark.
Bjørn H EbdrupCenter for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Center Glostrup, Copenhagen University Hospital - Amager and Hvidovre, Hvidovre, Denmark.
Karen S AmbrosenCenter for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Center Glostrup, Copenhagen University Hospital - Amager and Hvidovre, Hvidovre, Denmark.
Merete NordentoftCopenhagen Research Center for Mental Health (CORE), Mental Health Center Copenhagen, Hellerup, Denmark.
Louise B Glenthøj *Department of Psychology, University of Copenhagen, Copenhagen, Denmark.
Stella Grasshof *Data Science Section, IT University of Copenhagen, Copenhagen, Denmark.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Emotional processing deficits are increasingly recognized as a salient feature of psychotic disorders, and emerging evidence suggests that impairments in facial emotion processing may serve as additive predictive markers for individuals at ultra-high risk (UHR) for psychosis, highlighting the importance of such deficits for early identification and intervention strategies. In this study we examined whether automated coding of facial features from brief video recordings (45 seconds) could discriminate 108 individuals at ultra-high risk for psychosis (UHR) from 65 matched healthy controls (HC). We assessed the alignment between automated coding of facial expressivity as measured by granulated muscle activity, and clinician-rated facial emotional expressivity by experienced clinicians. In addition, we explored associations with social skills and clinical symptoms. A random forest classifier with repeated nested cross-validation significantly classified UHR-individuals from HC with above chance accuracy (63%;

Indexed as

Action units (AUs)automated registrationemotion expressionfacial featuresultra-high risk for psychosis

Identifiers

PMID41993838
PMCPMC13079383

What OpenQuestion holds

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Registered trials

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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.