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Record W4386174495 · doi:10.22259/2638-5201.0201011

Pseudodiagnoses of Malingering of Neuropsychological Symptoms in Survivors of Car Accidents by the Structured Inventory of Malingered Symptomatology

2019· article· en· W4386174495 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueArchives of Psychiatry and Behavioral Sciences · 2019
Typearticle
Languageen
FieldMedicine
TopicHealthcare Systems and Public Health
Canadian institutionsWestern University
Fundersnot available
KeywordsMalingeringNeuropsychologyPsychologyClinical psychologyPsychiatryCognition

Abstract

fetched live from OpenAlex

Background:The Structured Inventory of Malingered Symptomatology (SIMS) is a widely used test allegedly designed for detection of malingering medical symptomatology.Even a brief perusal of its 75 True-False items reveals that too many of these list legitimate medical symptoms, in particular, symptoms within the postconcussion-whiplash spectrum, as experienced by survivors of motor vehicle accidents (MVAs).The present study examined conceptual overlap of SIMS items with symptoms assessed by the Rivermead Post-Concussion Symptoms Questionnaire and also with symptoms assessed by the scale of Post-MVA Neurological Symptoms (PMNS). Materials and Method:De-identified archival data of 98 patients (mean age 42.2 years, SD=14.3,38 males, 60 females) containing scores on Rivermead and PMNS were tabulated to list frequencies of each endorsed symptom.Next to these symptoms, SIMS items were tabulated which conceptually overlap with legitimate postconcussive or post-whiplash neuropsychological signs listed in Rivermead and PMNS.Results: More than a half of the 75 SIMS items could be potentially endorsed by post-MVA patients due to their neuropsychological symptoms, without any intent to malinger.Each of the SIMS items counts one point towards a cutoff point of > 14: thus, the majority of post-MVA patients are likely to be misclassified by SIMS as malingerers.Many of the other remaining SIMS items could be endorsed by non-malingering patients with some other medical conditions such as acute schizophrenia, or low intelligence, etc. Almost no SIMS items appear suited to reliably differentiate malingerers from legitimate medical patients. Discussion and Conclusions:A thorough review of all 75 SIMS items suggests that most of them would not adequately differentiate non-malingering persons from malingerers: the items were included in the SIMS without their author's sufficient knowledge of the wide range of possible psychopathology and of other medical conditions.This is consistent with the lack of satisfactorily designed validation studies of the SIMS that would meet standards of the American Psychological Association for tests meant to perform diagnostic tasks.The rates of false positive with SIMS are unacceptably high: clinical use of SIMS implies malpractice.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.018
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.028
GPT teacher head0.350
Teacher spread0.321 · 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