Patient and clinician experiences of a computerised cognitive battery for use after concussion: a preliminary qualitative study
Bibliographic record
Abstract
Abstract Objective: The Cognition Battery of the National Institute of Health (NIH) Toolbox for Assessment of Neurological and Behavioural Function is a computerised neuropsychological battery recommended for clinical practice, neurological research and clinical trials. We investigated the utility of the NIH Toolbox Cognition Battery (NIHTB-CB) for people with concussion. Methods: In this small qualitative study, semi-structured interviews were conducted with five adults with concussion who were participating in a larger study using the NIHTB-CB. Three clinician participants and two cultural advisors familiar with the tool were also interviewed. Interview transcripts were analysed using a general thematic approach and qualitative description. Results: Participants described both positive and negative experiences with the NIHTB-CB and using qualitative description, their experiences were organised into three broad themes: (1) using technology for cognitive testing made sense, (2) there were some cultural relevance questions and (3) cognitive testing after concussion could have challenges. They were positive about the computerised format and range of domains assessed for the concussion context but identified the contextual relevance of some content as having potential to impact on performances. Conclusion: This was a small study examining the experiences of a select group of participants, but nevertheless does suggest a need for future research validating the NIHTB-CB for use in different cultural and clinical contexts.
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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 | 0.028 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".