Data Safety Monitoring Boards: Overview of Structure and Role in Spinal Cord Injury Studies
Bibliographic record
Abstract
This paper provides an overview of the history, composition, organization, responsibilities, and regulatory requirements of Data Safety Monitoring Boards (DSMB), with particular reference to the context of clinical trials in spinal cord injury. It is intended to help potential members of such boards and those undertaking the design of new clinical trials to understand the important role of the DSMB in safeguarding the integrity of complex trials, promoting safety, and countering potential bias. An independent DSMB helps to protect research subjects by providing study oversight and serves as an additional step to assure that clinical trials are performed to existing and appropriate standards. The DSMB must meet on a regular schedule, diligently evaluate all the information it receives, and report in a timely and decisive manner. Members must be free of significant conflicts of interest throughout the study and be adequately trained and experienced to serve their roles within the group. DSMB service can be a valuable learning experience and a gratifying opportunity to participate in advancing medicine and helping to maintain and improve the standards of research.
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 | 0.160 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".