Incorrect encoding responses improve the classification accuracy of the Word Choice Test
Bibliographic record
Abstract
This study investigated whether responses from the Word Choice Test (WCT) encoding trial could provide a supplemental index of performance validity in addition to the traditional Summary score. Participants were 196 adult outpatients who underwent neuropsychological evaluations for various referral reasons related to, but not limited to epilepsy, stroke, and age-related cognitive decline. Participants were classified into valid or invalid performance groups using a criterion-grouping approach based on multiple independent performance validity tests. We derived a supplemental validity indicator, entitled the "Encoding" score, based on the number of correct responses from 43 items on the initial WCT trial, which were identified via critical item analysis. Using cutoffs of ≤40 for the Encoding score and ≤42 for the Summary score together enhanced classification accuracy, yielding an area under the curve of .83. Compared to using the WCT Summary score alone, the combined use of the Encoding and Summary scores increased the sensitivity by .10 to yield a total sensitivity of .58, while maintaining high (.92) specificity. Findings suggest the WCT Encoding score may provide a useful index of performance validity alongside the Summary score. Employing these indicators together can optimize the WCT without adding cost or much time to the evaluation.
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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 | 0.006 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".