Future Directions for Work on Refinement of ADHD Assessment in Young Adults
Bibliographic record
Abstract
Sibley, Coxe, and Molina provide a thoughtful discussion of the implications of our study and highlight important future directions in this line of work. They helpfully amplify several themes that space did not allow discussion of in our article. In particular, they correctly emphasize the importance of theoretical as well as statistical considerations in model selection. We also agree that clinical tests of sensitivity and specificity, taking into account different base rates and types of samples, are essential before a final algorithm would be ready for dissemination. However, we are not convinced that such tests should be limited to populations of individuals with attention-deficit/hyperactivity disorder (ADHD). Rather, they should include those with and without diagnosed ADHD in order to provide comprehensive tests of reporter sensitivity and specificity across the entire continuum of ADHD symptomatology and in relation to different populations, including other disorders and typically developing populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".