Evidence map›Paper›PMID 27936001›Full record

ArticlePloS one2016

Computerized Analysis of Verbal Fluency: Normative Data and the Effects of Repeated Testing, Simulated Malingering, and Traumatic Brain Injury.

David L Woods, John M Wyma, Timothy J Herron, E William Yund

Registry-linked trialAbstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04800588 (Computerized Tests of Cognitive Decline in Presymptomatic Alzheimer's Disease), which is not on this map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT04800588 narecruitingnot on this mapstarted 2021, after this paper: background citation

Computerized Tests of Cognitive Decline in Presymptomatic Alzheimer's Disease

TypeinterventionalSponsorNeurobehavioral Systems, Inc.Ran2021 to 2027Enrolled2,000ConditionsAlzheimer Disease, Cognitive Decline, AgingArmsCalifornia Cognitive Assessment Battery
3 · Its place in the literature

Who cites it

22 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. The California Cognitive Assessment Battery (CCAB).Frontiers in human neuroscience · 2023
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. SNAFU: The Semantic Network and Fluency Utility.Behavior research methods · 2020
    Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

David L WoodsHuman Cognitive Neurophysiology Laboratory, VANCHCS, Martinez, CA, United States of America.ORCID http://orcid.org/0000-0002-8804-3587
John M WymaHuman Cognitive Neurophysiology Laboratory, VANCHCS, Martinez, CA, United States of America.
Timothy J HerronHuman Cognitive Neurophysiology Laboratory, VANCHCS, Martinez, CA, United States of America.
E William YundHuman Cognitive Neurophysiology Laboratory, VANCHCS, Martinez, CA, United States of America.

Funding

Quantitative automated lesion detection (QALD) in moderate and severe TBII01CX001000 · VA · VA NORTHERN CALIFORNIA HEALTH CARE SYS · PI DRONKERS, NINA F. · 2014 to 2017
–
CSRD VA I01 CX000583CSRD VA I01 CX001000
6 · The paper itself

Abstract

In verbal fluency (VF) tests, subjects articulate words in a specified category during a short test period (typically 60 s). Verbal fluency tests are widely used to study language development and to evaluate memory retrieval in neuropsychiatric disorders. Performance is usually measured as the total number of correct words retrieved. Here, we describe the properties of a computerized VF (C-VF) test that tallies correct words and repetitions while providing additional lexical measures of word frequency, syllable count, and typicality. In addition, the C-VF permits (1) the analysis of the rate of responding over time, and (2) the analysis of the semantic relationships between words using a new method, Explicit Semantic Analysis (ESA), as well as the established semantic clustering and switching measures developed by Troyer et al. (1997). In Experiment 1, we gathered normative data from 180 subjects ranging in age from 18 to 82 years in semantic ("animals") and phonemic (letter "F") conditions. The number of words retrieved in 90 s correlated with education and daily hours of computer-use. The rate of word production declined sharply over time during both tests. In semantic conditions, correct-word scores correlated strongly with the number of ESA and Troyer-defined semantic switches as well as with an ESA-defined semantic organization index (SOI). In phonemic conditions, ESA revealed significant semantic influences in the sequence of words retrieved. In Experiment 2, we examined the test-retest reliability of different measures across three weekly tests in 40 young subjects. Different categories were used for each semantic ("animals", "parts of the body", and "foods") and phonemic (letters "F", "A", and "S") condition. After regressing out the influences of education and computer-use, we found that correct-word z-scores in the first session did not differ from those of the subjects in Experiment 1. Word production was uniformly greater in semantic than phonemic conditions. Intraclass correlation coefficients (ICCs) of correct-word z-scores were higher for phonemic (0.91) than semantic (0.77) tests. In semantic conditions, good reliability was also seen for the SOI (ICC = 0.68) and ESA-defined switches in semantic categories (ICC = 0.62). In Experiment 3, we examined the performance of subjects from Experiment 2 when instructed to malinger: 38% showed abnormal (p< 0.05) performance in semantic conditions. Simulated malingerers with abnormal scores could be distinguished with 80% sensitivity and 89% specificity from subjects with abnormal scores in Experiment 1 using lexical, temporal, and semantic measures. In Experiment 4, we tested patients with mild and severe traumatic brain injury (mTBI and sTBI). Patients with mTBI performed within the normal range, while patients with sTBI showed significant impairments in correct-word z-scores and category shifts. The lexical, temporal, and semantic measures of the C-VF provide an automated and comprehensive description of verbal fluency performance.

Indexed as

Decision Making, Computer-AssistedAdolescentAdultAgedAged, 80 and overBrain Injuries, TraumaticFemaleHumansLanguageLanguage TestsMaleMalingeringMiddle AgedPhoneticsPsychomotor PerformanceSemantics

Identifiers

PMID27936001
PMCPMC5147824

What OpenQuestion holds

Textmetadata
LicenceCC0
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.