A Comparative Study of the National Infertility Registry System and the Proposed Model for Iran
Bibliographic record
Abstract
Objectives: The national registry system of infertility has been established at various levels in different countries over the years due to the high prevalence of infertility in the world, as well as its social and economic effects on communities. Therefore, the present study was conducted to provide a model of the National Infertility Registry System for Iran. Materials and Methods: This comparative study was conducted in 2016, and the sample data included those related to the infertility registry systems in the United States, Canada, and England. Based on the aim of the study, different articles, databases, books, and the related websites were searched and national and international experts were consulted with in order to investigate the infertility registry system in developed countries (e.g., the United States, Canada, and England). Then, based on economic, cultural, and geographical conditions of Iran, an infertility registry system was proposed including 7 main axes and 20 sub-axes. Finally, the proposed model was validated using the Delphi technique at two stages, showing an agreement coefficient of 85%. Results: In this study, the model for the Iranian National Registry System was proposed based on seven aspects encompassing the objectives and the structure of the system, data elements, the criterion of the registry, the process of data collection and reporting, as well as data quality control and classification. Conclusions: In general, due to the importance of the infertility registry in taking health measures, the proposed model can improve the management of infertile patients, in terms of providing a system to follow the results, and the effectiveness of the treatment, health family planning, and controlling the factors which influence infertility.
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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 | 0.030 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".