Information-analytical assessment systems for perinatal outcomes and children’s health status born by assisted reproductive technologies
Bibliographic record
Abstract
Purpose: To integrate clinical databases based on the information and analytical systems of medical organizations (MO) and to evaluate outcomes and health of children born by assisted reproductive technologies (ART). Research methods: To form database by integrating information and analytical systems of the medical organizations. To search and unify the data by means of freeware relational database management system (DBMS) – MySQL. Results. A prototype application for the management and support of a unified clinical database to analyze and evaluate the outcomes of ART. To compare information from individual databases of MO one need to match fields in the form of unique personal records, field validation, processing of missing data. Significant (p <0.001) differences in gestational age and anthropometric data in the databases of medical organizations (76% of full-term children from the Automated System “Regional obstetric monitoring” (AS ROM) database compared with 18% according to the IS “Register of Children requiring Early Care”) were determined by the introduction of the conception of ART, along with the presence of perinatal risk factors in children. The created software can be used to create registers of medical organizations, as well as to support clinical decision-making in forecasting, modeling outcomes and children’s health after ART, developing personalized treatment and rehabilitation programs. Conclusion. It is necessary to develop and implement information systems, to create ART registers on the basis of regional and national registers, to analyze the outcomes of the use of reproduction methods.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".