An updated review of the prevalence of invalid performance on the Immediate Post-Concussion and Cognitive Testing (ImPACT)
Bibliographic record
Abstract
Objective: Performance validity assessment is an important component of concussion baseline testing and Immediate Post-Concussion and Cognitive Testing (ImPACT) is the most commonly used test in this setting. A review of invalid performance on ImPACT was published in 2017, focusing largely on the default embedded validity indicator (Default EVI) provided within the test. There has since been a proliferation in research evaluating the classification accuracy of the Default EVI against independently developed, alternative ImPACT-based EVIs, necessitating an updated review. The purpose of this study was to provide an up-to-date review of the prevalence of invalid performance on ImPACT and to examine the relative effectiveness of ImPACT-based EVIs. Method: Literature related to the prevalence of invalid performance on ImPACT and the effectiveness of ImPACT-based EVIs, published between January 2000 and May 2020, was critically reviewed. Results: A total of 23 studies reported prevalence of invalid performance at baseline testing using ImPACT. Six percent of baseline assessments were found to be invalid by the ImPACT’s Default EVI, and between 22.31% and 34.99% were flagged by alternative EVIs. Six studies assessed the effectiveness of ImPACT-based EVIs, with the Default EVI correctly identifying experimental malingerers only 60% of the time. Alternative ImPACT-based EVIs identified between 73% and 100% of experimental malingerers. Conclusions: The ImPACT’s Default EVI is not sufficiently sensitive, and clinicians should consider alternative indicators when assessing invalid performance. Accordingly, the base rate of invalid performance in athletes at baseline testing is likely well above the 6% previously reported.
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 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 | 0.011 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.022 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".