Critical Item Analysis Enhances the Classification Accuracy of the Logical Memory Recognition Trial as a Performance Validity Indicator
Bibliographic record
Abstract
Objective : Replicate previous research on Logical Memory Recognition (LMRecog) and perform a critical item analysis.Method : Performance validity was psychometrically operationalized in a mixed clinical sample of 213 adults. Classification of the LMRecog and nine critical items (CR-9) was computed.Results : LMRecog ≤20 produced a good combination of sensitivity (.30-.35) and specificity (.89-.90). CR-9 ≥5 and ≥6 had comparable classification accuracy. CR-9 ≥5 increased sensitivity by 4% over LMRecog ≤20; CR-9 ≥6 increased specificity by 6–8% over LMRecog ≤20; CR-9 ≥7 increased specificity by 8–15%.Conclusions : Critical item analysis enhances the classification accuracy of the optimal LMRecog cutoff (≤20).
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".