SMArtCARE - A platform to collect real-life outcome data of patients with spinal muscular atrophy
Bibliographic record
Abstract
BACKGROUND: Survival and quality of life for patients affected by spinal muscular atrophy (SMA) are thought to have improved over the last decade due to changes in care. In addition, targeted treatments for SMA have been developed based on a better understanding of the molecular pathology. In 2016 and 2017, nusinersen was the first drug to be approved for treatment of all types of SMA in the United States and in Europe based on well-controlled clinical trials in a small subgroup of pediatric SMA patients. Systems are required to monitor treated and untreated SMA patients in a real-life environment to optimize treatment and care, and to provide outcome data to regulators, payers, and the SMA community. METHODS: Within SMArtCARE, we conduct a prospective, multicenter non-randomized registration and outcome study. SMArtCARE collects longitudinal data on all available SMA patients independent of their actual treatment regime as disease-specific SMA registry. For this purpose, we provide an online platform for SMA patients seen by health-care providers in Germany, Austria and Switzerland. All data are collected during routine patient visits. Items for data collection are aligned with the international consensus for SMA registries. Data analysis is carried out independent of commercial partners. CONCLUSION: A prospective monitoring of all SMA patients will lead to a better understanding of the natural history of SMA and the influence of drug treatment. This is crucial to improve the care of SMA patients. Further, we will establish a network for neuromuscular centers to share experience with SMA patients and to promote research projects on SMA. TRIAL REGISTRATION: German Clinical Trials Register ("Deutsches Register klinischer Studien") DRKS00012699. Registered 09 August 2018. https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00012699 .
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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".