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Record W4414427619 · doi:10.1080/02699052.2025.2554248

On the ecological validity of the behavioural assessment of dysexecutive syndrome measure in mild traumatic brain injury

2025· article· en· W4414427619 on OpenAlexaff
Eliyas Jeffay, Sanghamithra Ramani, Konstantine K. Zakzanis

Bibliographic record

VenueBrain Injury · 2025
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsEcological validityTraumatic brain injuryDysexecutive syndromeNeuropsychologyNeuropsychological assessmentMeasure (data warehouse)Psychometrics

Abstract

fetched live from OpenAlex

BACKGROUND: A growing concern in neuropsychology is whether neuropsychological test measures (NTMs) can predict functional outcome (i.e. ecological validity). The relationship between neuropsychological tests and return to work (RTW) outcomes following mild traumatic brain injury (mTBI) found that the majority of tests were either weakly or completely unrelated. As such, many have opined that clinical neuropsychology should adopt new tests that relate test performance to real-world functioning, such as the Behavioural Assessment of Dysexecutive Syndrome (BADS). Further investigation into the BADS sensitivity to employment status in a mTBI sample is needed. PRESENT STUDY: We aimed to investigate if the BADS is better at differentiating between employment status compared to traditional pen-and-paper neuropsychological test measures in a sample of patients in the post-acute period of recovery after mTBI. RESULTS: Following correction of family-wise error, neither the BADS nor traditional tests could differentiate employment status in patients with mTBI who were in the post-acute period of recovery. CONCLUSIONS: The lack of significant findings in the majority of the tests highlights the importance of other facets of a complete neuropsychological assessment. Furthermore, researchers may benefit from investigating other forms of assessment that could prove to be more ecologically valid.

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 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)0.004
metaresearch head score (Gemma)0.004
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 score0.374
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.153
GPT teacher head0.407
Teacher spread0.254 · 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 teacher head, 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

Citations0
Published2025
Admission routes1
Has abstractyes

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