National Institutes of Health Consensus Development Project on Criteria for Clinical Trials in Chronic Graft-versus-Host Disease: I. The 2020 Etiology and Prevention Working Group Report
Bibliographic record
Abstract
Preventing chronic graft-versus-host disease (GVHD) remains challenging because the unique cellular and molecular pathways that incite chronic GVHD are poorly understood. One major point of intervention for potential prevention of chronic GVHD occurs at the time of transplantation when acute donor anti-recipient immune responses first set the events in motion that result in chronic GVHD. After transplantation, additional insults causing tissue injury can incite aberrant immune responses and loss of tolerance, further contributing to chronic GVHD. Points of intervention are actively being identified so that chronic GVHD initiation pathways can be targeted without affecting immune function. The major objective in the field is to continue basic studies and to translate what is learned about etiopathology to develop targeted prevention strategies that decrease the risk of morbid chronic GVHD without increasing the risks of cancer relapse or infection. Development of strategies to predict the risk of developing debilitating or deadly chronic GVHD is a high research priority. This working group recommends further interrogation into the mechanisms underpinning chronic GVHD development, and we highlight considerations for future trial design in prevention trials.
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 | 0.116 | 0.170 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".