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Record W4367187051 · doi:10.21203/rs.3.rs-2848762/v1

Accuracy of the Clock Drawing Test in Screening for Early Post-Stroke Neurocognitive Disorder: The Nor-COAST Study

2023· preprint· en· W4367187051 on OpenAlexaboutno aff
Egle Navickaite, Ingvild Saltvedt, Stian Lyndersen, Ragnhild Munthe‐Kaas, Hege Ihle‐Hansen, Ramunė Grambaitė, Stina Aam

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersFaculty of Medicine and Health, University of SydneySt. Olavs Hospital Universitetssykehuset i TrondheimHaukeland UniversitetssjukehusNasjonalforeningen for FolkehelsenNorges Teknisk-Naturvitenskapelige Universitet
KeywordsMontreal Cognitive AssessmentNeurocognitiveStroke (engine)Receiver operating characteristicMedicineProspective cohort studyCognitionCognitive impairmentPhysical therapyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Abstract BACKGROUND Post-stroke neurocognitive disorder (NCD), though common, is often overlooked by clinicians. Moreover, although the Montreal Cognitive Assessment (MoCA) has proven to be a valid screening test for NCD, even more time saving tests would be preferred. In our study, we examined the accuracy of the Clock Drawing Test (CDT) in diagnosing patients with post-stroke NCD and the association between the CDT and MoCA. METHODS This study is part of the Norwegian Cognitive Impairment After Stroke study, a multicentre prospective cohort study following patients admitted with acute stroke. At the three-month follow-up, patients were classified with normal cognition, mild NCD or major NCD according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. Any NCD compromised both mild- and major NCD. The CDT, as part of the MoCA at the three-month assessment, was given scores ranging from 0 to 5. Patients able to complete the CDT and whose cognitive status could be classified were included in analyses. The CDT accuracy for diagnosing post-stroke NCD was examined using receiver operating characteristic (ROC) curves, sensitivity, specificity, positive predictive value, and negative predictive value. The association between the MoCA and CDT was analysed with Spearman’s rho. RESULTS Of 554 participants, 238 (43.0%) were women. Mean (SD) age was 71.5 (11.8) years, while mean (SD) National Institutes of Health Stroke Scale score was 2.6 (3.7). The area under the ROC curve of the CDT for major NCD and any NCD was 0.73 (95% CI, 0.68–0.79) and 0.68 (95% CI, 0.63–0.72), respectively. A CDT cutoff of < 5 yielded 68% sensitivity and 60% specificity for any NCD and 78% sensitivity and 53% specificity for major NCD. Spearman’s correlation coefficient between scores on the MoCA and CDT was 0.50 (95% CI, 0.44–0.57, p < .001). CONCLUSIONS The CDT is not accurate enough to diagnose post-stroke NCD but shows reasonable accuracy in identifying major NCD. Performance on the CDT was associated with performance on MoCA; however, the CDT is inferior to MoCA in identifying post-stroke NCD. TRIAL REGISTRATION ClinicalTrials.gov (NCT0265053)

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)0.004
metaresearch head score (Gemma)0.013
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 score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.085
GPT teacher head0.428
Teacher spread0.343 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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