Expert Insights: Phase III Clinical Trials in Non-Transfusion-Dependent (ENERGIZE) and Transfusion-Dependent (ENERGIZE-T) α- and β-Thalassaemia
Bibliographic record
Abstract
The thalassaemias are a heterogeneous group of inherited chronic blood disorders associated with impaired haemoglobin (Hb) synthesis, resulting in ineffective erythropoiesis, haemolysis, and the development of lifelong anaemia. The pathophysiology of thalassaemia is due, in part, to increased energy demand to clear globin aggregates and reactive O2 species, and maintain overall red blood cell (RBC) health, coupled with insufficient adenosine triphosphate (ATP) production. For this article, interviews were conducted by EMJ in December 2022 with two key opinion leaders. Kevin Kuo is a Clinician-Investigator and Staff Haematologist at the University Health Network, Toronto, Canada, and Associate Professor in the Division of Hematology, Department of Medicine, University of Toronto, Canada. Maria Cappellini is Professor of Medicine in the Department of Clinical Sciences and Community, University of Milan, Italy. Both haematologists have over 50 years of expertise and clinical experience between them in treating patients with thalassaemia. The two experts provided insights into two ongoing Phase III clinical trials for both α- and β-thalassaemia. These trials are investigating the effect of mitapivat, a first-in-class, small molecule pyruvate kinase activator, in patients across the full range of thalassaemia subtypes. The ENERGIZE trial is investigating mitapivat in patients with non–transfusion-dependent thalassaemia (NTDT), and the ENERGIZE-T trial is investigating mitapivat in patients with transfusion-dependent thalassaemia (TDT).
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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 | 0.029 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".