The Standard for the Exchange of Nonclinical Data (SEND): Challenges and Promises
Bibliographic record
Abstract
The Standard for the Exchange of Nonclinical Data (SEND) is an implementation of the Study Data Tabulation Model for nonclinical studies that enables the U.S. Food and Drug Administration (FDA) to modernize and streamline the review process. As a result, patients may benefit from speedier approval of new drugs. However, SEND implementation and compliance can be challenging and require effective cooperation between pharmaceutical companies and contract research organizations. In order to improve Society of Toxicologic Pathology (STP) members' awareness about SEND, including the steps, obstacles, and mistakes to avoid in its implementation while applying for FDA approval, the Career Development and Outreach Committee of the STP sponsored a career development lunchtime series panel discussion entitled "The Standard for the Exchange of Nonclinical Data (SEND): Challenges and Promise" in conjunction with the STP 37th Annual Symposium. The presentations and discussion at this workshop provided perspectives of experts including pathologists and information technology professionals familiar with the SEND submission process and FDA reviewers. This article is designed to provide brief summaries of their talks as well as the questions asked during this well-received panel discussion.
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 | 0.656 | 0.653 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.012 | 0.026 |
| Scholarly communication | 0.038 | 0.044 |
| Open science | 0.012 | 0.023 |
| Research integrity | 0.026 | 0.053 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".