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Record W2898020984 · doi:10.1212/wnl.0000000000006544

Who should undergo a comprehensive cognitive assessment after a stroke?

2018· article· en· W2898020984 on OpenAlexaboutno aff
Olivier Godefroy, Hugo Yaïche, H. Taillia, Flavie Bompaire, Claudine Nédélec-Ciceri, Camille Bonnin, Jérôme Varvat, Françoise Vincent-Grangette, Momar Diouf, Jean‐Louis Mas, Sandrine Canaple, Chantal Lamy, Audrey Arnoux, Claire Leclercq, Sophie Tasseel‐Ponche, Martine Roussel, Mélanie Barbay, Sandrine Despretz-Wannepain, Virginie Tourbier, Annie Thorel-Routier, Pascal Despretz, Hassan Berrissoul, Carl Picard, Gwenolé Loas, H. Deramond, Jean‐Marc Constans, Véronique Quaglino, Hélène Beaunieux, Christine Moroni, Audrey Martens-Chazelles, Stéphanie Batier-Monperrus, Cécile Monteleone, Véronique Costantino, Eric Theunssens

Bibliographic record

VenueNeurology · 2018
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsLogistic regressionMedicineConfidence intervalOdds ratioModified Rankin ScaleStroke (engine)CognitionNeurocognitiveInternal medicinePhysical therapyMontreal Cognitive AssessmentCognitive impairmentPsychiatryIschemic stroke

Abstract

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Objective To validate the ability of a specifically developed cognitive risk score to identify patients at risk of poststroke neurocognitive disorders (NCDs) who are eligible for a comprehensive cognitive assessment. Methods After assessing 404 patients (infarct 91.3%) in the Groupe de Réflexion pour l9Evaluation Cognitive VASCulaire (GRECogVASC) cross-sectional study with the National Institute of Neurological Disorders and Stroke–Canadian Stroke Network battery 6 months after stroke, we used multivariable logistic regression and bootstrap analyses to determine factors associated with NCDs. Independent, internally validated factors were included in a cognitive risk score. Results Cognitive impairment was present in 170 of the 320 patients with a Rankin Scale score ≥1. The backward logistic regression selected 4 factors (≥73% of the permutations): NIH Stroke Scale score on admission ≥7 (odds ratio [OR] 2.73, 95% confidence interval [CI] 1.29–4.3, p = 0.005), multiple strokes (OR 3.78, 95% CI 1.6–8, p = 0.002), adjusted Mini-Mental State Examination (MMSEadj) score ≤27 (OR 6.69, 95% CI 3.9–11.6, p = 0.0001), and Fazekas score ≥2 (OR 2.34, 95% CI 1.3–4.2, p = 0.004). The cognitive risk score computed with these 4 factors provided good calibration, discrimination (overoptimism-corrected C = 0.793), and goodness of fit (Hosmer-Lemeshow test p = 0.99). A combination of Rankin Scale score ≥1, cognitive risk score ≥1, and MMSEadj score ≥21 selected 230 (56.9%) of the 404 patients for a comprehensive assessment. This procedure yielded good sensitivity (96.5%) and moderate specificity (43%; positive predictive value 0.66, negative predictive value 0.91) and was more accurate (p ≤ 0.03 for all) than the sole use of screening tests (MMSE or Montréal Cognitive Assessment). Conclusion The GRECogVASC cognitive risk score comprises 4 easily documented factors; this procedure helps to identify patients at risk of poststroke NCDs who must therefore undergo a comprehensive assessment. ClinicalTrials.gov identifier: NCT01339195.

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)0.001
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: Commentary · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.033
GPT teacher head0.342
Teacher spread0.309 · 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
GenreCommentary

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

Citations21
Published2018
Admission routes1
Has abstractyes

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