An Alternative Approach to Scoring the MTA-SNAP-IV to Guide Attention-Deficit/Hyperactivity Disorder Medication Treatment Titration towards Symptom Remission: A Preliminary Consideration
Bibliographic record
Abstract
OBJECTIVE: The Multimodal Treatment Study for Attention-Deficit/Hyperactivity Disorder Swanson, Nolan, and Pelham, Version IV (MTA-SNAP-IV) is a common rating scale to measure attention-deficit/hyperactivity disorder (ADHD) symptoms during medication treatment. Relying on the traditional scoring approach for this instrument to identify symptom remission, however, may leave a child with significant residual symptoms. The objective of this study was to examine an alternative scoring approach for this instrument to identify the extent of residual symptoms for children completing ADHD medication treatment. METHODS: Parent and teacher ratings on the ADHD symptom component of the MTA-SNAP-IV were extracted from medical records of 80 children completing medication treatment at a specialty clinic in Canada. Data were scored in two ways. 1) Traditional scoring based on assigning a value ranging from 0 to 3 for response options: "Not at all," "Just a little," "Pretty much," or "Very much," for each symptom and then determining a mean across items, and 2) alternative scoring based on assigning values of 0, 0, 0.5, and 1 across the same response options and summing the total across items. Symptom remission based on the former is defined as a mean value ≤ 1, and for the latter it is defined as a summed value equal to 0. RESULTS: Children were significantly less likely to be classified as symptom remitted under the alternative scoring method based on parent, teacher, and combined parent-teacher ratings. Using the alternative scoring approach, residual symptoms were identified for 25%, 39%, and 70% of children classified as symptom remitted (under traditional scoring rules) by parents, teachers, and parents/teachers combined, respectively. CONCLUSIONS: Potential "residual" ADHD symptoms were identified in many children attaining symptom remission using the traditional scoring approach; however, further scrutiny of this alternative scoring approach is required. Although it may improve the ability to detect residual symptoms that could signal the need for further intervention to achieve symptom remission, it may increase the risk of over treatment.
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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 | 0.045 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".