ABBA Letter Alternation: A telehealth inspired measure of executive functioning/inhibitory control
Bibliographic record
Abstract
Objective: To introduce ABBA Letter Alternation (ABBA) as a computerized measure of response inhibition/response alternation developed for telehealth following restrictions of in-person testing due to COVID-19. ABBA consists of two PowerPoint-administered trials: Letter Reading of 25 capital As or Bs individually presented, and Letter Alternation with instructions to say the opposite letter to what is presented. Method: We obtained initial normative ABBA performance from 899 healthy research volunteers participating in the Emory Healthy Brain Study (EHBS) with Montreal Cognitive Assessment (MoCA) scores 24/30 and higher. Cutpoints derived from the EHBS sample were applied to a series of 32 Parkinson disease (PD) patients being evaluated for deep brain stimulation to provide preliminary clinical validation. Trail Making B (TMT B) was also examined in both groups. Results: 775 (86.2%) EHBS participants made 0–1 ABBA Letter Alternation errors, 58 (6.5%) EHBS participants had 2 ABBA alternation errors, and 66 (7.3%) made 3+ errors. Applying these thresholds to PD patients, 22 (68.8%) made 0–1 alternation errors, 3 PD (9.4%) patients made 2 errors, and 7 PD subjects (21.8%) made 3+ errors, which significantly differed in frequency from the EHBS group (χ2=9.8, p=.007). EHBS vs. PD differed on MoCA, a medium effect (p<.00001; η2=.076), and on TMT B (p<.00001; η2=.158), which is considered a large effect. Conclusion: These results provide initial support for ABBA Letter Alternation as a response inhibition/response alternation. Application in larger clinical samples, in both telehealth and face-to-face settings, will be needed to more fully establish ABBA’s clinical utility.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".