How do we achieve blinding in modern electronic and paper medical records during the conduct of transfusion trials?
Bibliographic record
Abstract
BACKGROUND: Regulatory aspects of transfusion medicine add complexity in blinded transfusion trials when considering various electronic record keeping software and blood administration processes. The aim of this study is to explore strategies when blinding transfusion components and products in paper and electronic medical records. METHODS: Surveys were collected and interviews were conducted for 18 sites across various jurisdictions in North America to determine solutions applied in previous transfusion randomized control trials. RESULTS: Sixteen responses were collected of which 11 had previously participated in a transfusion randomized control trial. Various solutions were reported which were specific to the laboratory information system (LIS) and electronic medical record (EMR) combinations although solutions could be grouped into four categories which included the creation of a study product code in the LIS, preventing the transmission of data from the LIS to the EMR, utilizing specialized stickers and labels to conceal product containers and documents in the paper records, and modified bedside procedures and documentation. DISCUSSION: LIS and EMR combinations varied across sites, so it was not possible to determine combination-specific solutions. The study was able to highlight solutions that may be emphasized in future iterations of LIS and EMR software as well as procedural changes that may minimize the risk of unblinding.
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 | 0.841 | 0.902 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.017 | 0.030 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".