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Record W3110924047 · doi:10.1002/alz.041963

Neuropsychiatric symptoms in subjective cognitive complaints (SCC) and mild cognitive impairment (MCI): Detecting changes over time with the Mild Behavioral Impairment Checklist (MBI‐C)

2020· article· en· W3110924047 on OpenAlexaff
Sabela C. Mallo, Arturo X. Pereiro, María Campos‐Magdaleno, Ana Nieto‐Vieites, Cristina Lojo‐Seoane, David Façal, Zahinoor Ismail, Onésimo Juncos‐Rabadán

Bibliographic record

VenueAlzheimer s & Dementia · 2020
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsChecklistDementiaNeuropsychologyCognitive impairmentAnalysis of varianceRepeated measures designCognitionPsychologyMedicineClinical psychologyInternal medicinePsychiatryDisease

Abstract

fetched live from OpenAlex

Abstract Background Neurobehavioral Symptoms (NPS) have been usually measured with the Neuropsychiatric‐Inventory (NPI‐Q) (Kaufer et al., 2000) in pre‐dementia individuals. However, this instrument was designed to dementia states. The Mild Behavioral Impairment Checklist (MBI‐C) (Ismail et al., 2017) is an instrument developed to evaluate NPS in pre‐dementia states, including Mild cognitive impairment (MCI) and people with Subjective Cognitive Complaints (SCC). Evidence of longitudinal behavioral change obtained with the MBI‐C is still scarce. Our objective was to compare NPS scores, measured with the NPI‐Q and the MBI‐C, in MCI and SCC participants, at follow‐up. Method Two hundred forty participants from the Compostela Ageing Study recruited from primary care health centers, were classified into two groups, MCI (84) and SCC (166). Socio‐demographic, neuropsychological and NPS measures, including MBI‐C and NPI‐Q total scores on severity, were collected at baseline and at follow‐up (mean interval, 24 months) (Table 1). Instrument (NPI‐Q and MBI‐C) total score differences as a function of measurement Time (baseline vs follow‐up) were analyzed using repeated measure ANOVAs including Group (SCC vs MCI) as inter‐subject factor. Results Significant main effects of Time, F(1, 251)=7.68, p = .006, ηp 2=.030, observed power=.789, and Group, F(1, 251)=9.88, p = .002, ηp 2=.038, observed power=.879, were observed for the NPI‐Q total scores (Figure 1). Time*Group interaction was not found. Mixed ANOVA for the MBI‐C total score showed significant main effect of Group, F(1, 248)=13.93, p < .001, ηp 2=.053, observed power=.961, and Time*Group interaction, F(1, 248)=4.89, p = .028, ηp 2=.020, observed power=.596. Post hoc Bonferroni tests for the MBI‐C total score showed (Figure 2) significantly higher behavioral impairment in MCI than in SCC group in both time measurements (baseline and follow‐up). Conclusions Time main effect in NPI‐Q total scores showed decreases in severity of behavioral symptoms in the follow‐up measurement in spite of the Group (SCC vs MCI). On the contrary, MBI‐C scores pointed‐out slight increases in the follow‐up measurement and, considering the descriptive trends, particularly in MCI group. Only the MBI‐C was able to detect significant differences between MCI and SCC groups both in baseline and follow‐up measurements.

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)0.003
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.025
GPT teacher head0.298
Teacher spread0.273 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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