Collaborative data collection by TREAT-NMD Registries to support post-marketing surveillance in Spinal Muscular Atrophy
Bibliographic record
Abstract
Introduction TREAT-NMD is a neuromuscular network that aims to ensure that the most promising new therapies reach patients as quickly as possible. The TREAT-NMD Global Network of Spinal Muscular Atrophy (SMA) Registries include 50 national registries that collect a common core dataset and are governed by the TREAT-NMD Global Database Oversight Committee (TGDOC). Researchers and industry can request anonymised and aggregate data, offering a single point of access to this extensive dataset. Results The core dataset was established 10 years ago when the main purpose of the registries was clinical trial readiness and recruitment. In the current SMA landscape, with emerging treatments and new therapeutic approaches in development, there is a need for more widespread longitudinal data collection to support future research and post marketing surveillance (PMS) requirements for emerging therapies. To support this, TREAT-NMD are reviewing and expanding the core dataset for their SMA Registries. A workshop was held in May 2017 involving expert clinicians, physiotherapists, registry curators, patient representatives and other stakeholders from across the world, who developed a proposed expanded dataset containing 38 data items. A pilot feasibility study with the new dataset will be run in a sub-group of SMA registry sites (n=12). Conclusions Feedback from the pilot sites has been collated and discussed during a second workshop in June 2018, to make recommendations on (a) the content and structure of the expanded core dataset, and (b) the timescales, costs, and considerations for the full-scale roll-out to all 50 TREAT-NMD SMA Registries.
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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 | 0.420 | 0.520 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".