Academic Productivity from Rare Neuromuscular Disease Registries: A Systematic Review
Bibliographic record
Abstract
Background: TREAT-NMD is a global neuromuscular (NM) organization, created to enhance infrastructure to facilitate novel therapeutics reaching patients. One main activity is aimed at supporting NM disease registries. These rare disease registries are useful to fill knowledge gaps for various stakeholders in the disease community using real world data. Although it is important to understand how patient data is being utilized in the TREAT-NMD network and other rare disease registries, there is no systematic process or consistent metric for documenting the academic output from these registries. Objectives: The objective of this study was to determine the academic output from NM registries associated with the TREAT-NMD network, and the types of research the data is facilitating. Results: A systematic search of EMBASE, Medline, Cochrane Central and SCOPUS was performed from inception to November 24, 2021. The search yielded a total of 650 results, with 231 full text studies assessed for eligibility and a total of 97 studies that met the inclusion criteria. Conclusions: The results suggest publications from TREAT-NMD are mainly descriptive or methodologic. Rare disease registries, like the TREAT-NMD network, would benefit from clear and consistent metrics to facilitate reporting of academic output.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".