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Record W2972153599 · doi:10.1093/neuonc/noz126.131

P08.05 Cognitive impairment in patients with newly-diagnosed high-grade gliomas

2019· article· en· W2972153599 on OpenAlexaboutno aff
Monica Ribeiro, Thomas Durand, J. Jacob, Dimitri Psimaras, G. Noël, L. Feuvret, Khê Hoang‐Xuan, Martine Roussel, Olivier Godefroy, Marie‐Odile Bernier, D. Ricard

Bibliographic record

VenueNeuro-Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicCancer-related cognitive impairment studies
Canadian institutionsnot available
Fundersnot available
KeywordsDysexecutive syndromeCognitionLogistic regressionMontreal Cognitive AssessmentMedicineExecutive dysfunctionStepwise regressionInternal medicineExecutive functionsEffects of sleep deprivation on cognitive performancePsychologyOncologyAudiologyPsychiatryCognitive impairmentNeuropsychology

Abstract

fetched live from OpenAlex

Abstract BACKGROUND Cognitive dysfunction is frequent in patients with primary brain tumor, impairing attention, memory and executive function. It compromises functional independence, decision making capacity and psycho-social well-being. Cognitive functioning is highly correlated to disease progression and quality of survival, thus cognitive follow-up is essential in the management of the disease. Cognitive screening tools are often used, since a comprehensive battery may be time consuming and challenging for patients. The objective of this study was to identify a pattern of cognitive dysfunction in patients with newly-diagnosed high-grade gliomas and evaluate the sensitivity and specificity of the MoCA (Montreal Cognitive Assessment) as a cognitive screening tool in the clinical practice. MATERIAL AND METHODS We compared performances in tests of memory, action speed, visuospatial ability and executive function of 156 patients with newly-diagnosed WHO Grade III and IV gliomas, after surgery and prior to radiochemotherapy, to those of a group of healthy controls (n=1003). Relatives assessed behavior through a questionnaire of behavioral dysexecutive syndrome. A stepwise logistic regression was performed to select cognitive domains better discriminating patients from healthy controls and we tested the sensitivity and specificity of the MOCA using ROC curve analysis. RESULTS The stepwise logistic regression analysis identified the 3 following factors better discriminating patients from controls: TMT-B completion time (OR: 0.673; 95% CI: 0.511–0.886; p=0.0005), a verbal memory index (OR:0.507; 95% CI: 0.358–0.718; p=0.0001) and a behavioral dysexecutive score (OR:0.616; 95% CI: 0.468–0.812, p=0.001). Prevalence of cognitive-behavioral impairment was of 35.94%; 95% CI: 28.3 - 43.5. The ROC curve analysis for the assessment of the MoCA sensitivity and specificity in detecting impairment yielded 0.795 (95%CI: 0.714–0.875) for the MoCA raw score, and 0.804 (95%CI: 0.727 - 0.881) for the adjusted z score. The optimal discrimination was obtained for a raw score ≤ 25 (sensitivity of 0.526; specificity of 0.832). For the adjusted score, optimal discrimination value was observed with a -0.603 z score (sensitivity of 0.716; specificity of 0.768). CONCLUSION Cognitive impairment and behavioral dysexecutive syndrome is frequent in patients with newly-diagnosed high-grade glioma. The MoCA lacks sensitivity in screening cognitive impairment to discriminate patients from healthy controls in this setting, and a comprehensive neuropsychological assessment is still recommended.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.274
Teacher spread0.263 · 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".

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Citations2
Published2019
Admission routes1
Has abstractyes

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