Assessing TDApp: An AI-based clinical decision support system for ADHD treatment recommendations
Bibliographic record
Abstract
Introduction: Clinical practice guidelines (CPGs) have several limitations, namely: obsolescence, lack of personalization, and insufficient patient participation. These factors may contribute to suboptimal treatment recommendation compliance and poorer clinical outcomes. APPRAISE-RS is an adaptation of the GRADE heuristic designed to generate CPG-like treatment recommendations that are automated, updated, personalized, participatory, and explanatory using a symbolic AI approach. TDApp is a clinical decision support system (CDSS) that implements APPRAISE-RS for ADHD. Methods: Two clinical trials were conducted. In both studies a total of 33 and 32 ADHD patients, respectively, requiring treatment initiation or a major treatment change were enrolled. TDApp recommendations were compared to those of selected CPGs (American Academy of Pediatrics, National Institute for Health and Care Excellence, Spanish Health System, Canadian ADHD Resource Alliance, and the Australasian ADHD Professionals Association) CPGs. The diversity of treatment recommendations was analyzed using Blau's index. Concordance between TDApp and CPGs recommendations was assessed by calculating the proportion of patients for whom TDApp recommended one drug that was also endorsed by CPGs. Dendrograms were plotted to compare the distance between treatment recommendations as calculated using the NbN nomenclature. Results: The first study investigated eight methods that differed in how patient and clinician preferred outcomes were handled and the extent to which TDApp tailored the analysis of evidence. The method deemed most suitable was examined in the second study, which found that 50-75% of the patients received at least one favorable treatment recommendation. TDApp evaluated over 10 drugs, including recently marketed ones, with amphetamine derivatives emerging as the most frequently recommended interventions. TDApp generated 8-12 distinct treatment recommendations with a diversity index of 0.70-0.88, which was higher than those of CPGs. The proportion of patients for whom TDApp recommendations overlapped with at least one drug endorsed by CPGs ranged from 21.9% to 100%. Dendrogram analysis revealed that TDApp was positioned on one side of the tree, while CPGs clustered together on the opposite side. Conclusions: TDApp is an advanced prototype of an CDSS offering automated, participatory, personalized, and explanatory treatment recommendations for ADHD. It represents a promising alternative to CPGs for aiding clinicians and patients in shared treatment decision-making.
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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 | 0.015 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".