Establishing a minimum data set for Parkinson's (PMDS) in Iran
Bibliographic record
Abstract
BACKGROUND: The minimum data set (MDS) is one of the important steps in the development of health care information systems. According to the Ministry of Health in Iran, a central and national registry along with Parkinson's MDS (PMDS) has not yet existed. So, this research was conducted to establish a PMDS in Iran. MATERIAL AND METHODS: This study was a descriptive-comparative method, which was done in 2019-2021 in four phases: (1) determining data elements related to Parkinson's disease in Iran and selected countries; (2) extracting and categorizing the data elements; (3) making a PMDS draft; (4) evaluating a draft by Delphi technique. The research population was the MDS in Australia, Canada, the United States of America, and Iran. After extracting the data elements of Parkinson's disease from various resources, the primary draft PMDS was developed. Then, the research group divided it into two categories (administrative and clinical). After that, it was sent to 50 healthcare professionals for validation by the Delphi method. RESULTS: Following the results of the two rounds of Delphi technique, Finally, PMDS was established including a total of 223 data elements in two categories: administrative and clinical with 72 and 151, respectively. Every category included 10 and 14 subcategories. CONCLUSION: The first and the most important step for standardization of data collection nationally is creating MDS. Due to the necessity of the existence of PMDS, a complete list of PMDS was established for collecting data on Parkinson's 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 | 0.068 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".