An International Registry of Granulocyte Transfusions
Bibliographic record
Abstract
INTRODUCTION: Granulocyte transfusions are used to either treat or prevent life-threatening infections in neutropenic patients. Current evidence from clinical trials does not support or reject efficacy, nor guide practice. METHODS: A group of investigators have led the efforts to create an online registry to gather information on granulocyte transfusion practices from as broad a range of international settings. The data forms were adapted from an on-going study in England for electronic data management. Data is collected at the time of the request for granulocytes, weekly, at 28 days, and at 6 months. Information collected includes donor, granulocyte unit, patient and illness characteristics, and outcomes. RESULTS: The PROspective GRanulocyte usage and outcomEs Survey (ProGrES) is currently open for data entry. Centres across the UK have collected data on 80 subjects. Five institutions from 4 countries (2 from the US, 1 each from Brazil, and national services in Canada and France) are in the process of joining the study. Other countries have expressed interest. CONCLUSION: It is feasible to develop an international registry of granulocyte transfusions to characterise current practices and describe outcomes. This registry would provide a platform to explore the relationship between intervention and outcomes, and to generate evidence to inform granulocyte transfusion efficacy.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".