A web-based system for managing and co-ordinating multiple multisite studies
Bibliographic record
Abstract
Efficient and secure collection and management of information is essential in any modern biomedical study. Data management and coordination of multisite studies is a complex process. It involves development of systems for data collection, data cleaning with quality assurance checks, and specimen tracking, as well as development of procedures for conducting the study, training clinical sites, and communicating with sites to answer study questions and resolve and track data inquiries and resolutions. We developed a secure web-based system that is designed to automate evaluation of eligibility criteria and data collection, track specimens, serve as a resource for study-specific information, facilitate communication across sites in multisite studies, track data queries and resolutions, and allow administrative management of studies. The system combines a common framework across studies that defines the internal structure for all the web pages, with a study-specific one that defines the content of each page via a relational database. This combination creates a flexible and efficient environment enabling several multisite studies to be simultaneously or consecutively implemented and managed in a timely manner. We describe the development process, the system and its evaluation, current status, lessons learned, and future development plans.
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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.017 |
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".