Methodological challenges in outcomes research for early-trials for implementation of new therapies in neuropediatric rare diseases
Bibliographic record
Abstract
During 2023, the leads of the ECNP TWG focused on Clinical Outcomes in Early-Trials in Neurosciences scheduled two brainstorming sessions to allow a deep discussion about challenges facing neurosciences drug development in neuropediatric rare diseases. Sessions were led by Dr Maria T. Acosta from the Undiagnosed Disease Program (UDP) at the National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, EE.UU. The experts discussed about challenges and potential solutions as well as alternative options to design appropriated for clinical outcomes assessment (COA) instruments for this population. An important discussion took place due to extensive expertise of the participants and hands-on experience with clinical trials. Participation from experts from several disciplines was a key factor for appraising and brainstorm innovative solutions in the field. Identification of challenges and propose of innovative solutions were a central core of the discussion. Despite of the increasing number of rare and ultra-rare diseases being identify by the day, and the significant differences in biology, and clinical presentation, it is clear most of them face similar problems when is time to select the appropriated COAs to test potential interventions, and experts have a limited potential to drive efficient solutions. Some of the commonly identified common problems include: small number of patients affected, disease changes over time, developmental aspects impacting the clinical presentation according with age, individual variability in terms of disease severity between patient, variable window for intervention and in some cases, need to expedite treatment as per the disease progression. All these and other features, require an extensive dialogue and continue communication between preclinical researchers, clinicians, patients and family members, pharma and treatment designers and regulatory agencies in each condition, making this process, expensive, time consuming and very difficult to accomplish. A feasible solution, that may be applicable to several conditions, is to develop a “back bone” structure to approach rare diseases, allowing each “disease team” to tailored assessments and study design, according with the specific features of the condition. We concluded that it is fundamental to establish effective bridges of communication between the different actors implicated in the clinical trial design and execution, sharing experiences, as well as clear understanding of meaningful outcomes not only for researchers, but clinicians, patients and families. Important emphasis is done in the need or careful selection of COAs in each individual condition to be able to obtain more efficient and reliable results in clinical trials in this population. We need to be creative, and there is a need to leave the “comfort zone” of current methodologies and start piloting new methods at clinical settings. A specific research platform was proposed as a solution in the creation, sharing and validation of new assessment instruments, which would be available to clinicians and researchers attending small patient samples distributed over the world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.827 | 0.863 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.013 | 0.019 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".