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Record W4307494781 · doi:10.2196/33871

An App-Based Digit Symbol Substitution Test for Assessment of Cognitive Deficits in Adults With Major Depressive Disorder: Evaluation Study

2022· article· en· W4307494781 on OpenAlexaffvenue
Roger S. McIntyre, Orly Lipsitz, Nelson B. Rodrigues, Mehala Subramaniapillai, Flora Nasri, Yena Lee, Ben Fehnert, James R. King, Lambros Chrones, Kevin Kratiuk, Sharif Uddin, Joshua D. Rosenblat, Rodrigo B. Mansur, Maggie McCue

Bibliographic record

VenueJMIR Mental Health · 2022
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsUniversity of TorontoBrain and Cognition Discovery FoundationUniversity Health Network
Fundersnot available
KeywordsDigit symbol substitution testNeurocognitiveCognitionMajor depressive disorderClinical psychologyPsychologyPsychiatryAudiologyMedicinePlacebo

Abstract

fetched live from OpenAlex

BACKGROUND: Cognitive dysfunction is an impairing core symptom of depression. Among adults with major depressive disorder (MDD) treated with antidepressants, residual cognitive symptoms interfere with patient-reported outcomes. The foregoing characterization of cognitive symptoms provides the rationale for screening and assessing the severity of cognitive symptoms at point of care. However, clinical neurocognitive assessments are time-consuming and difficult, and they require specialist expertise to interpret them. A smartphone-delivered neurocognitive test may offer an effective and accessible tool that can be readily implemented into a measurement-based care framework. OBJECTIVE: We aimed to evaluate the use of a smartphone-delivered app-based version of the established Cognition Kit Digit Symbol Substitution Test (DSST) neurocognitive assessment compared to a traditional paper-and-pencil version. METHODS: Convergent validity and test-retest reliability of the 2 versions were evaluated. Patient satisfaction with the app was also assessed. RESULTS: Assessments made using the app-based Cognition Kit DSST were highly correlated with the standard paper-and-pencil version of the test, both at the baseline visit (r=0.69, df=27; P<.001) and at the end-of-study visit (r=0.82, df=27; P<.001), and they were positively evaluated by 30 patients as being user-friendly, easy to navigate, and preferable over the paper-and-pencil version of the DSST. However, although the app-based Cognition Kit DSST was validated in patients with MDD, it still needs to be evaluated in healthy controls. CONCLUSIONS: App-based DSST may facilitate a more personalized, convenient, and cost-effective method of cognitive assessment, helping to guide measurement-based care and psychotherapeutic and pharmacologic treatment options for patients with MDD. TRIAL REGISTRATION: ClinicalTrials.gov NCT03999567; https://tinyurl.com/2p8pnyv7.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.448
Teacher spread0.408 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2022
Admission routes2
Has abstractyes

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