A comprehensive review of the Paced Auditory Serial Addition Test (PASAT)
Bibliographic record
Abstract
The Paced Auditory Serial Addition Test (PASAT) was developed to assess the effects of traumatic brain injury (TBI) on cognitive functioning. Subsequent research has shown that the PASAT has clinical utility in detecting impairments in cognitive processing in patients with a wide variety of neuropsychological syndromes. Gronwall and Sampson (1974) originally assumed the PASAT measured speed of information processing. However, the PASAT is now recognized as a measure of multiple functional domains because it requires the successful completion of a variety of cognitive functions, primarily those related to attention. While the PASAT has demonstrated good psychometric properties such as high levels of internal consistency and test-retest reliability, several issues should be considered when administering and interpreting this test. For example, test-retest scores show that the PASAT is extremely susceptible to practice effects. The PASAT is also negatively affected by increasing age, decreasing IQ, and low math ability. Administration of the PASAT creates an undue amount of anxiety and frustration in participants which affects their performance on this and other neuropsychological tests, and may subsequently increase their reluctance to return for follow up testing. Demands for rapid responding place individuals with speech or language impairment at a distinct disadvantage, as it does for those who naturally speak slowly for cultural or geographic reasons. In conclusion, the PASAT represents a reliable test that has legitimate but restricted clinical applications. A low score on the PASAT may not necessarily indicate or confirm the presence of neurological pathology. The PASAT is a highly sensitive, non-specific test and as such, care must be taken to identify the reasons underlying any low score before interpreting it as clinically significant.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".