Derivation of New Embedded Performance Validity Indicators for the Child and Adolescent Memory Profile (ChAMP) Objects Subtest in Youth with Mild Traumatic Brain Injury
Bibliographic record
Abstract
BACKGROUND: Development of an embedded performance validity test (PVT) is desired for visual memory tests. The goal of this study was to derive an embedded PVT for the Child and Adolescent Memory Profile (ChAMP) Objects visual memory subtest in youth with mild traumatic brain injury (MTBI). METHODS: Children and adolescents (N = 91; mean age = 14.9 years, SD = 2.2, range = 8-18) on average 25.2 weeks (SD = 15.4) post-MTBI were administered ChAMP Objects. Two stand-alone PVTs (Test of Memory Malingering and Medical Symptom Validity Test) were administered, which allowed for grouping into valid (zero failed stand-alone PVTs) and invalid (both stand-alone PVTs failed). Cutoff scores for invalid performance on ChAMP Objects and Objects Delayed were established using failure on two PVTs as the criterion. RESULTS: One in five youth (n = 19) failed both PVTs. Invalid performance was not associated with demographics or time since injury, but was significantly correlated with both ChAMP Objects (r = .53, p<.001) and Objects Delayed (r = -.63, p<.001). Area under the curve suggested adequate discrimination by Objects (.87) and excellent discrimination by Objects Delayed (.91). A cutoff scaled score of 5 or less on ChAMP Objects provided sensitivity of 58% for detecting invalid performance with 96% sensitivity. A cutoff scaled score of 5 or less on ChAMP Objects Delayed achieved sensitivity of 63% and specificity of 96%. Interpreting the two embedded PVTs simultaneously improved sensitivity to 79% with 93% specificity. CONCLUSION: This study yields promising new embedded PVTs for the ChAMP Objects subtest with strong sensitivity and specificity for detecting invalid performance in youth with MTBI.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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 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".