MétaCan
Menu
Back to cohort
Record W2622429320 · doi:10.1055/s-0043-103267

The Stroop-Interference-NoGo-Test (STING): A Fast Screening Tool for the Global Assessment of Neuropsychological Impairments

2017· article· en· W2622429320 on OpenAlexaboutno aff
Bernhard Fehlmann, Hennric Jokeit

Bibliographic record

VenueNeurology International Open · 2017
Typearticle
Languageen
FieldPsychology
TopicCognitive Functions and Memory
Canadian institutionsnot available
Fundersnot available
KeywordsStroop effectNeuropsychologyCognitionPsychologyMontreal Cognitive AssessmentReliability (semiconductor)AudiologyNeuropsychological assessmentTest (biology)Cognitive psychologyDiscriminative modelExecutive functionsClinical psychologyCognitive impairmentMedicineComputer sciencePsychiatryArtificial intelligencePower (physics)

Abstract

fetched live from OpenAlex

Abstract Background With the Stroop-Interference-NoGo-Test (STING), we introduce an efficient and sensitive screening tool for the assessment of mild to moderate cognitive impairment. Its development was motivated by the ongoing economization of diagnostics and therapy in clinics as well as by the increased recognition of the effects of cognitive impairments on quality of life and professional reintegration. Established screenings such as the MoCA, MMSE and CAMCOG are either more time-consuming or lack sensitivity with regard to mild to moderate impairments in relevant domains. Methods STING is based on the idea of an omnibus test. It integrates attentional, lexical-semantic, speed- and inhibitory components. In this way, a basic sensorimotor component is separated from a higher-order cognitive/executive component, which allows for differentiation between cognitive and generalised or merely sensorimotor impairments. The norms are based on data from 907 participants (386 M, 521 F). Its discriminative power was investigated in 64 patients (32 M, 32 F) with heterogeneous, but predominantly mild to moderate neuropsychological impairments. Results The split-half reliability is essentially r=0.82–0.95. For the parallel-test reliability, the index is r=0.82–0.91, whereas the test-retest stability is estimated somewhat lower (r=0.48–0.81). Practice effects are moderate (7–12%). STING is correlated with many familiar tests, but sets itself apart from mere intelligence testing. Within the age category of 12–34 years, the number of correct items in the more complex second half of the test was predictive for clinical caseness, with a sensitivity of 83% and a specificity of 47%. Between the ages of 35 and 64, the classification was improved by the combination with the ratio of both halves, which represents set-shifting costs. Here the sensitivity of 71% goes hand in hand with a specificity of 70%. Discussion STING provides a measure that can be considered sufficiently sensitive for use in the global assessment of cognitive impairment. A positive result does not replace a neuropsychological assessment, but indicates the need for one. The test offers an opportunity to neurologists, psychologists and psychiatrists to objectify mild to moderate, transient, or chronic functional impairments and to evaluate their course over time.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.089
GPT teacher head0.442
Teacher spread0.354 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueNeurology International OpenSame topicCognitive Functions and MemoryFrench-language works237,207