A Model for Spinal Muscular Atrophy Disease Registry for Iran
Bibliographic record
Abstract
Objective(s): Spinal muscular atrophy is a rare genetic disease of neuromuscular and it is considered the main cause of death of newborns, which affects spinal motor neurons. The variety of degrees of this disease and the lack of a complete and integrated information recording system hinder the quality of providing care, early diagnosis and timely treatment. Therefore, the disease registry is considered as a supplement to the patient's medical record. The purpose of this research is to provide a model of spinal muscular atrophy registry in Iran. Methods: First, using a descriptive-comparative method, the characteristics of national registries (Pakistan, Czech Republic, Australia, and Canada) and international registries (Translational Research in Europe, Assessment & Treatment of Neuromuscular Diseases (TREAT-NMD), Smart Care, and RESTORE) for spinal muscular atrophy were examined and compared. Then, the initial model proposed for the spinal muscular atrophy registration system was designed for Iran and was validated by experts using the Delphi method in two rounds. The research community included 15 experts with expertise in medical informatics, health information management, neurologists and medical genetics. Finally, the cases that obtained more than 75% agreement were included in the final model and the cases less than 50% were removed from the model. Results: Out of 79 components have been agreed by experts, 58 components in the first round of Delphi and five components in the second round of Delphi achieved a collective agreement of over 75 percent. Therefore, the final model of the spinal muscular atrophy registry in Iran included eight dimensions, and 63 components. The registry system characteristics for the final model were categorized into the following dimensions: objective, structure (registry type, implementation method and participating organizations), data source (primary and secondary), data collection (method, responsible, data collection location, and data registration criteria), data quality control (evaluation methods and data quality characteristics), security (data access and security methods), data analysis, and reporting and information dissemination (reporting methods, representation, and reporting intervals). Conclusion: It is expected that the presented model can be effective in improving the outcome management of spinal muscular atrophy disease, providing better services, achieving an integrated information system and facilitating research.
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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 | 0.025 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".