The effects of mild and severe traumatic brain injury on the auditory and visual versions of the Adjusting-Paced Serial Addition Test (Adjusting-PSAT)
Bibliographic record
Abstract
Auditory and visual versions of the Adjusting-PSAT [Tombaugh, T. N. (1999). Administrative manual for the adjusting-paced serial addition test (Adjusting-PSAT). Ottawa, Ontario: Carleton University] were used to examine the effects of mild and severe traumatic brain injury (TBI) on information processing. The Adjusting-PSAT, a computerized modification of the original PASAT [Gronwall, D., & Sampson, H. (1974). The psychological effects of concussion. Auckland, New Zealand: Auckland University Press], systematically varied the inter-stimulus interval (ISI) by making the duration of the ISI contingent on the correctness of the response. This procedure permitted calculation of a temporal threshold measure that represented the fastest speed of digit presentation at which a person was able to process the information and provide the correct answer. Threshold values progressively declined as a function of the severity of TBI with visual thresholds significantly lower than auditory thresholds. The major importance of the current study is that the threshold measure offers a potentially more precise way of evaluating how TBI affects cognitive functioning than is achieved using the traditional PASAT and the number of correct responses. The Adjusting-PSAT offers the additional clinical advantages of eliminating the need to make a priori decisions about what ISI should be used in different clinical applications, and avoiding spuriously high levels of performance that occur when an "alternate answer" or chunking strategy is used. Unfortunately, the Adjusting-PSAT did not reduce the high level of frustration previously associated with the traditional PASAT.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".