Empowering Families Through Technology: A Mobile-Health Project to Reduce the TAND Identification and Treatment Gap (TANDem)
Bibliographic record
Abstract
Tuberous Sclerosis Complex (TSC) is a multi-system genetic disorder with various TSC-Associated Neuropsychiatric Disorders (TAND) that significantly impact the mental health and wellbeing of individuals with TSC and their caregivers. TAND represents the number one concern to families worldwide, yet is highly under-identified and under-treated. The clinician-administered TAND-Checklist (Lifetime version, TAND-L) has improved identification of TAND in clinical settings. However, many individuals with TSC and their caregivers still have difficulty accessing suitable support for diagnosis and evidence-informed interventions. The TANDem study is a community-based participatory research project with a broad range of TSC stakeholders aimed at reducing the TAND identification and treatment gap.Participatory research identified three priority next steps: 1) development and validation of a self-report, quantified version of the TAND Checklist (TAND-SQ) and building the TAND-SQ into a smartphone application, 2) generation of consensus clinical recommendations for the identification and treatment of TAND, to be incorporated as a TAND toolkit on the app, and 3) establishment of a global TAND consortium through networking, capacity-building and public engagement activities.TANDem is a four-year project, and includes 24 consortium members from 10 countries representing all World Health Organization regions. Collaborators represent five stakeholder groups (family representatives, technology experts, clinical experts, non-profit organisations and researchers). Here we outline the project study protocol in detail, describing the scientific rationale, the project aims and objectives, the methods involved in participant recruitment, multi-site and multi-phase data collection, data analysis, ethical considerations including informed consent, data protection, privacy and confidentiality considerations related to the European Union General Data Protection Regulation and the USA Health Insurance Portability and Accountability Act. The expected outcomes and potential impact on the TSC community, implementation and dissemination of results, as well as future scale-up and scale-out plans are also discussed.The TANDem project has the potential to transform the global TSC community by empowering families living with TSC through an easily accessible digital solution to allow them to document their own TAND needs linked to an evidence-informed toolkit to enhance personalised healthcare, and by providing healthcare professionals with consensus clinical recommendations to prevent, identify and manage TAND manifestations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".