Evaluation of the effectiveness of two clinical training procedures to elicit yes/no responses from patients with a severe acquired brain injury: a randomized single-subject design
Bibliographic record
Abstract
PRIMARY OBJECTIVE: Thirteen (10 males) participants with severe acquired brain injuries (ABI) were randomly assigned to two treatments, A or B (ABAB, BABA) in a crossover study to determine which treatment approach elicited more consistent and reliable yes/no responses. RESEARCH DESIGN: Treatment A consisted of an enriched stimulus environment, collaborative multidisciplinary interventions and additional yes/no response training, while Treatment B consisted of the standard hospital environment and interventions. MAIN OUTCOMES: An ANOVA showed no order effect (AB vs BA; p=0.60), but a trend (A vs B;p=0.07) towards statistical significance for increased responsiveness with treatment A. Inter-raterreliability (n=10) ranged from fair-to-good, intra class correlation (ICC) 0.51; 95% confidence interval (CI) (0.29-0.93). Post-hoc analyses showed statistically significant increased responsiveness for four participants with treatment A (p<0.001). CONCLUSION: Evidence is provided that enhanced communication strategies can improve responsiveness in a sub-group of participants with severe acquired brain injuries.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".