Developing an Executive Functioning Composite Score for Research and Clinical Trials
Bibliographic record
Abstract
OBJECTIVE: Executive functioning encompasses interactive cognitive processes such as planning, organization, set-shifting, inhibition, self-monitoring, working memory, and initiating and sustaining motor and mental activity. Researchers therefore typically assess executive functioning with multiple tests, each yielding multiple scores. A single composite score of executive functioning, which summarizes deficits across a battery of tests, would be useful in research and clinical trials. This study examines multiple candidate composite scores of executive functioning using tests from the Delis-Kaplan Executive Function System (D-KEFS). METHOD: Participants were 875 adults between the ages of 20 and 89 years from the D-KEFS standardization sample. Seven Total Achievement scores were used from three tests (i.e., Trail Making, Verbal Fluency, and Color-Word Interference) to form eight composite scores that were compared based on their psychometric properties and association with intelligence (IQ). RESULTS: The distributions of most composite scores were mildly to severely skewed, and some had a pronounced ceiling effect. The composite scores all showed a medium positive correlation with IQ. The composite scores were highly intercorrelated in the total sample and in four IQ subgroups (i.e., IQ <89, 90-99, 100-109, 110+), with some being so highly correlated that they appear redundant. CONCLUSIONS: This study is part of a larger research program developing a cognition endpoint for research and clinical trials with sound psychometric properties and utility across discrepant test batteries. Future research is needed to examine the reliability and ecological validity of these composite scores.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.102 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".