Developing Cognition Endpoints for the CENTER-TBI Neuropsychological Test Battery
Bibliographic record
Abstract
Background: Measuring cognitive functioning is common in traumatic brain injury (TBI) research, but no universally accepted method for combining several neuropsychological test scores into composite, or summary, scores exists. This study examined several possible composite scores for the test battery used in the large-scale study Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI). Methods: Participants with mild TBI (n=140), orthopedic trauma (n=72), and healthy community controls (n=70) from the Trondheim MTBI follow-up study completed the CENTER-TBI test battery at two weeks after injury, which includes both traditional paper-and-pencil tests and tests from the Cambridge Neuropsychological Test Automated Battery (CANTAB). Seven composite scores were calculated for the paper and pencil tests, the CANTAB tests, and all tests combined (i.e., 21 composites): the overall test battery mean (OTBM); global deficit score (GDS); neuropsychological deficit score-weighted (NDS-W); low score composite (LSC); and the number of scores ≤5th percentile, ≤16th percentile, or <50th percentile. Results: The OTBM and the number of scores <50th percentile composites had distributional characteristics approaching a normal distribution. The other composites were in general highly skewed and zero-inflated. When the MTBI group, the trauma control group, and the community control group were compared, effect sizes were negligible to small for all composites. Subgroups with versus without loss of consciousness at the time of injury did not differ on the composite scores and neither did subgroups with complicated versus uncomplicated MTBIs. Intercorrelations were high within the paper-and-pencil composites, the CANTAB composites, and the combined composites and lower between the paper-and-pencil composites and the CANTAB composites. Conclusion: None of the composites revealed significant differences between participants with MTBI and the two control groups. Some of the composite scores were highly correlated and may be redundant. Additional research on patients with moderate to severe TBIs is needed to determine which scores are most appropriate for TBI clinical trials.
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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".