Bibliographic record
Abstract
treatment for acute GVHD. 5 A notable point is that both proteins showed reasonably large differences between responders and nonresponders in this study.Also interesting is that the B cell marker and IL-6 showed the largest differences between the groups, although B cells are thought to be conventionally more important in chronic GVHD than in acute GVHD.The biological implications of these observations need further study.As acknowledged by the authors, the biomarker panel could be optimized further, without losing much precision, by selecting biomarkers with large principal component coefficients.Panels with fewer biomarkers would be more realistic for routine clinical use.What were the most relevant biomarkers for predicting treatment response specifically to ruxolitinib?The authors explored interaction analyses and found no differential response in biomarker subgroups between the treatment arms.Thus, the current models predict treatment response regardless of treatment type, and further studies are warranted to identify biomarkers that predict treatment response specifically to ruxolitinib.An important goal now is to define ruxolitinib-refractory or -dependent patients with GVHD.A new working definition was proposed recently, with at least 14 days of treatment recommended to define lack of improvement.6 How will the models reported here inform practice?An important point to recognize is that this approach is a probability engine that tells us probabilities of response, rather than a classification engine that tells us positive and negative predictive values in predicting response.By using the probability engine, we will know an expected probability of response in an individual patient with steroid-refractory or steroid-dependent acute GVHD.Such information may help inform our clinical decision when we need to start second-line systemic treatment.Furthermore, if we can predict the probability of subsequent response based on clinical and biomarker information at day 14 or even earlier, this prediction may help us in initiating thirdline treatment earlier.Future studies should clarify reliable cutoff probabilities for treatment choice or change.Verification is needed in independent cohorts to prove the applicability and utility of the current models for use in clinical trials and in practice.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".