Reliability and validity of the RBANS in a traumatic brain injured sample
Bibliographic record
Abstract
The RBANS has become increasingly utilized in various populations since it reliably assesses individual neurocognitive domains in a rapid, efficient manner. The present study examined the convergent validity of the RBANS to frequently administered instruments in a moderate-severe traumatic brain injured (M-S TBI) sample. Fifty-seven individuals who sustained a M-S TBI were included in this study. The RBANS subtests showed moderate to strong internal reliability within the sample. Most of the subtests displayed moderate to strong correlations with the other neuropsychological tests, including the CVLT-II, COWAT, and WAIS-III subtests. The strongest correlations were within the RBANS Attention Index, with both the Digit Span and Coding subtests showing strong correlations with their WAIS-III counterparts. The RBANS measures distinct abilities that supplement other neuropsychological instruments that assess similar functions within a TBI sample. In addition to its administration advantages, the results of this study provide support for the use of the RBANS as a clinical valid and reliable tool in the brief screening of individuals with M-S TBI.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".