Leveraging Hematopoietic Cell Transplant Data and Biorepository Resources at the Center for International Blood and Marrow Transplant Research to Improve Patient Outcomes
Bibliographic record
Abstract
Hematopoietic cell transplantation (HCT) has undergone many advances over the decades. Trends in HCT utilization have been impacted by research based on the data and samples collected by the Center for International Blood and Marrow Transplant Research (CIBMTR). Here, we provide a summary report of the CIBMTR Biorepository resource and describe the biospecimen inventory along with collection and request procedures. The diversity captured in this inventory reflects transplant activity, and these samples can be leveraged for secondary analyses to generate more data and insights to advance the field. We describe how our resources have already impacted HCT practice and elaborate on possibilities for further collaboration and utilization to maximize capabilities and research opportunities. Hematopoietic cell transplant data and biorepository resources at the CIBMTR have been and continue to be leveraged to improve patient outcomes.
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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 | 0.056 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.037 | 0.011 |
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".