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Record W4390079161 · doi:10.1017/s1355617723008883

30 Examining the Base Rates of Low Scores in Older Adults with Subjective Cognitive Impairment from a Specialist Memory Clinic

2023· article· en· W4390079161 on OpenAlexaff
Karl S Grewal, Rory Gowda-Sookochoff, Andrew Kirk, Debra Morgan, Megan E. O’Connell

Bibliographic record

VenueJournal of the International Neuropsychological Society · 2023
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMemory spanMemory impairmentAudiologyCognitionNeuropsychological assessmentNeuropsychologyPsychologyVerbal learningCognitive impairmentCognitive testClinical psychologyMedicineWorking memoryPsychiatry

Abstract

fetched live from OpenAlex

Objective: Cognitively healthy individuals who complete a neuropsychological test battery can obtain very low scores. These very low scores are not likely indicative of cognitive impairment but are rather considered spuriously low scores. The expected number of low scores varies based on number and type of neuropsychological tests. Typically, base rates have been determined from normative samples, which could differ from samples seen in clinical settings. The current study reports on base rates of spuriously low cognitive scores in older adults presenting to a memory clinic who were diagnosed with subjective cognitive impairment after interprofessional assessment and information from collateral informants ruled out objective cognitive impairment. Participants and Methods: Base rates of spuriously low scores for a neuropsychological battery of 12 scores were based on 92 cognitively healthy older adults presenting to a specialist memory clinic (M(age) = 61.00, SD = 12.00; M(edu) = 12.00, SD = 2.74). Crawford’s Monte Carlo simulation algorithm was used to estimate multivariate base rates by calculating the percentage of cognitively healthy memory clinic patients who produced age and education normed scores at or below the 5th percentile. The following tests were used to produce the 12 scores: block design, digit span backwards, and coding from the WAIS-IV; logical memory I and II from the WMS-IV; immediate and delayed memory scores from the California Verbal Learning Test Second Edition short form; immediate and delayed memory scores from the Brief Visuospatial Memory Test Revised; category switching, letter number sequencing, and inhibition switching from the Delis Kaplin Executive Functioning System. Results: An estimated 33.58% of the cognitively healthy memory clinic population would have one or more low scores (5th percentile cutoff),14.7% would have two or more low scores, 6.55% would have three or more, 2.94% would have four or more, and 1.31% percent would have 5 or more very low scores due to chance. Conclusions: Determining base rates of spuriously low scores on a neuropsychological battery in a clinical sample of referred older adults with subjective memory complaints could assist in the diagnostic process. By understanding base rates of clinical samples, clinicians can use empirical data to adjust for expected low scores rather than using conventional corrections (such as 1/20 test scores expected to be low). In a memory clinic sample, three or more low test scores out of 12 is expected to be relatively rare in those who were later determined to have no objective evidence of cognitive impairment based on interprofessional assessment. Understanding normal frequency of low scores will prevent undue conclusions of cognitive impairment which will minimize false positives in diagnosis.

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)0.017
metaresearch head score (Gemma)0.083
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 score0.017
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.083
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.381
Teacher spread0.296 · 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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