Replicating a Meta-Analysis: The Search for the Optimal Word Choice Test Cutoff Continues
Why this work is in the frame
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Bibliographic record
Abstract
This study was designed to expand on a recent meta-analysis that identified ≤42 as the optimal cutoff on the Word Choice Test (WCT). We examined the base rate of failure and the classification accuracy of various WCT cutoffs in four independent clinical samples ( N = 252) against various psychometrically defined criterion groups. WCT ≤ 47 achieved acceptable combinations of specificity (.86–.89) at .49 to .54 sensitivity. Lowering the cutoff to ≤45 improved specificity (.91–.98) at a reasonable cost to sensitivity (.39–.50). Making the cutoff even more conservative (≤42) disproportionately sacrificed sensitivity (.30–.38) for specificity (.98–1.00), while still classifying 26.7% of patients with genuine and severe deficits as non-credible. Critical item (.23–.45 sensitivity at .89–1.00 specificity) and time-to-completion cutoffs (.48–.71 sensitivity at .87–.96 specificity) were effective alternative/complementary detection methods. Although WCT ≤ 45 produced the best overall classification accuracy, scores in the 43 to 47 range provide comparable objective psychometric evidence of non-credible responding. Results question the need for designating a single cutoff as “optimal,” given the heterogeneity of signal detection environments in which individual assessors operate. As meta-analyses often fail to replicate, ongoing research is needed on the classification accuracy of various WCT cutoffs.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it