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Record W2919192685 · doi:10.1016/j.nrleng.2018.12.022

Assessment of the diagnostic accuracy and discriminative validity of the Clock Drawing and Mini-Cog tests in detecting cognitive impairment

2021· article· en· W2919192685 on OpenAlexaboutno aff
Cristóbal Carnero Pardo, I. Rego-García, J.M. Barrios-López, S. Blanco-Madera, R. Calle-Calle, Samuel López-Alcalde, Rosa Vílchez-Carrillo

Bibliographic record

VenueNeurología (English Edition) · 2021
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaReceiver operating characteristicCogMedicineArea under the curveDiagnostic accuracyMontreal Cognitive AssessmentNeurologyCognitive impairmentInternal medicineCognitionCognitive testPsychologyPsychiatryDiseaseArtificial intelligence

Abstract

fetched live from OpenAlex

The Mini-Cog is a very brief, widely used cognitive test that includes a memory task and a simplified assessment of the Clock Drawing Test (CDT). There is not a formal evaluation of the Mini-Cog test in Spanish. This study aims to analyse the diagnostic usefulness of the Mini-Cog and CDT for detecting cognitive impairment (CI). We performed a cross-sectional study, systematically including all patients who consulted at our neurology clinic over a 6-month period. We assessed diagnostic usefulness for detecting CI (defined according to the National Institute on Aging-Alzheimer’s Association criteria for mild cognitive impairment and dementia) according to the area under the receiver operating characteristic curve (AUC). Sensitivity, specificity, and positive and negative likelihood ratios were calculated for each cut-off point. The study included 581 individuals (315 with CI); 55.1% were women and 27.7% had not completed primary studies. The Mini-Cog showed greater diagnostic usefulness than the CDT (AUC ± sensitivity: 0.88 ± 0.01 vs 0.84 ± 0.01; P < .01). Both instruments were less useful for screening in individuals with a low education level (0.74 ± 0.05 vs 0.75 ± 0.05, respectively). A cut-off point of 2/3 in the Mini-Cog achieved a sensitivity of 0.90 (95% CI, 0.87-0.93) and a specificity of 0.71 (95% CI, 0.65-0.76); a cut-off point of 5/6 in the CDT achieved a sensitivity of 0.77 (95% CI, 0.72-0.81) and a specificity of 0.80 (95% CI, 0.75-0.85). In our neurology clinic, the Mini-Cog showed acceptable diagnostic usefulness for detecting CI, greater than that of the CDT; neither test is an appropriate instrument for individuals with a low level of education. El Mini-Cog es un test cognitivo muy breve de uso extendido que incluye una tarea de memoria y una evaluación simplificada del Test del Reloj (TdR). No existe una evaluación formal del Mini-Cog en español; nuestro objetivo es analizar la utilidad diagnóstica (UD) del Mini-Cog y del TdR para deterioro cognitivo (DC). Estudio transversal en el que se han incluido de forma sistemática todos los sujetos atendidos durante un semestre en una consulta de Neurología. La UD se ha evaluado para DC (incluye sujetos con criterios NIA-AA de mild cognitive impairment o demencia) por medio del área bajo la curva ROC (aROC). Se han calculado los parámetros de sensibilidad (S), especificidad (E) y cocientes de probabilidad positivo y negativo (CP+, CP-) para los distintos puntos de corte. Se han incluido 581 sujetos (315 DC), 55.1% mujeres y 27.7% con bajo nivel educativo (< estudios primarios). La UD del Mini-Cog es superior a la del TdR (0.88 ± 0.01 (aROC ± ee) vs 0.84 ± 0.01, P < .01); para ambos instrumentos, la UD disminuye notablemente en sujetos con bajo nivel educativo (0.74 ± 0.05 y 0.75 ± 0.05 respectivamente). El punto de corte 2/3 del Mini-Cog tiene una S 0.90 (0.87−0.93) y una E 0.71 (0.65−0.76) y el 5/6 del TdR una S 0.77 (0.72−0.81) y E 0.80 (0.75−0.85). En consulta de Neurología, el Mini-Cog tiene una UD para DC aceptable, superior a la del TdR; ninguno de ellos es un instrumento adecuado para ser utilizado en sujetos con bajo nivel educativo.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.321
Teacher spread0.299 · 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 teacher head, not a consensus.

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

Citations25
Published2021
Admission routes1
Has abstractyes

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