Editorial: Precision medicine approaches for heterogeneous conditions such as autism spectrum disorders (The need for a biomarker exploration phase in clinical trials - Phase 2m)
Bibliographic record
Abstract
Editorial on the Research Topic Precision medicine approaches for heterogeneous conditions such as autism spectrum disorders (The need for a biomarker exploration phase in clinical trials -Phase m) Significant progress has been made in understanding the biology of autism spectrum disorder (ASD), providing rational hypotheses for interventions to address the core symptoms.However, clinical trials of these interventions have failed to yield positive results to date.In many of these studies, a subset of participants appear to respond well, but a significant benefit is not found in the overall intent-to-treat group.Due to the etiological heterogeneity of ASD, we anticipate that this will continue to be a challenge in future clinical trials.It will be critical to identify the patients that are most likely to respond to a treatment and to target those subjects in later phase trials.We Frontiers in Psychiatry frontiersin.orgBeversdorf et al. ./fpsyt. .therefore propose the explicit inclusion of "Phase 2m" as part of the pathway of clinical drug development, specifically for the development of a biomarker profile that can be incorporated into later phase 2 and 3 clinical trials.Such a precision medicine approach has the potential to optimize the likelihood of success in future clinical trials to benefit patients.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.019 | 0.023 |
| Insufficient payload (model declined to judge) | 0.014 | 0.014 |
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".