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Record W120357763

Assessment of Suboptimal Effort Using the CVLT-II Recognition Foils: A Known-Groups Comparison

2011· article· en· W120357763 on OpenAlexaff
Matias Mariani

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2011
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsUnivariateLogistic regressionSensitivity (control systems)Multivariate analysisMultivariate statisticsMedicinePsychologyInternal medicineAudiologyStatisticsMathematics
DOInot available

Abstract

fetched live from OpenAlex

The present study sought to generate an embedded effort index within the CVLT-II yes/no recognition trial using a known-groups design. Four types of recognition foils--i.e., novel/semantically unrelated (UN), novel/semantically related (PR), list B/semantically unrelated (BN), and list B/semantically related (BS)--as well as two composites--i.e., easy to reject foils (ETR) and difficult to reject foils (DTR)--were evaluated on their ability to distinguish between a group of 82 outpatients with moderate-severe traumatic brain injuries (TBI) and a group of 31 litigants meeting Slick et al. (1999) criteria for malingered neurocognitive dysfunction (MND). Separate multiple logistic regression analyses were performed. The full model based on the 4 foils correctly classified 88.5% of cases (61.3% sensitivity/98.8% specificity). The full model based on the composites correctly classified 81.4% of cases (45.2% sensitivity/95.1% specificity). With respect to univariate predictors, UN correctly classified 51.6-64.5% of MND cases and 90.2-100% of TBI cases depending on the diagnostic cut-off used. ETR also showed good classification accuracy (25.8-51.6% sensitivity/90.2-100% specificity). Three different ratios were generated from the original analyses--UN/PR, UN/(PR+BN+BS), and ETR/DTR. All three ratios yielded good to excellent diagnostic accuracy (87% sensitivity/98.4% specificity, 70.4% sensitivity/97% specificity, and 38.5% sensitivity/95.5% specificity, respectively). In addition, UN, ETR, and the multivariate equations were cross-validated with a group of 19 patients with complicated mild TBI supplying adequate effort (MTBI) and a group of 23 patients with complicated mild TBI performing poorly on effort measures (SE), resulting in high specificity values depending on the cut-offs used. Finally, previous research using the CVLT and CVLT-II (Coleman et al., 1998; Curtis et al., 2006; Millis et al., 1995; Millis et al., 2007; Sweet et al., 2000) was replicated. Overall, UN, ETR, both multivariate equations, and all three ratios derived from the foils of the CVLT-II yes/no recognition trial show considerable merit as embedded effort indices. Predictive power values are provided for all predictors at various diagnostic cut-offs across 5 hypothetical base rates to facilitate generalization of findings to different settings. Clinical and forensic implications are discussed with a focus on differential diagnoses.

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)0.005
metaresearch head score (Gemma)0.016
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 score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.195
GPT teacher head0.348
Teacher spread0.153 · 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
Published2011
Admission routes1
Has abstractyes

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