Treat Iron-Related Childhood-Onset Neurodegeneration (TIRCON)—An International Network on Care and Research for Patients With Neurodegeneration With Brain Iron Accumulation (NBIA)
Bibliographic record
Abstract
In order to improve clinical care, coordinate research activities and raise awareness for the ultra-orphan Neurodegeneration with Brain Iron Accumulation (NBIA) disorders, a group of NBIA clinicians and researchers, industry partners and patient advocacies from six European countries, Canada and the US joined forces in 2010 to set-up the collaborative initiative TIRCON (Treat Iron-Related Childhood-Onset Neurodegeneration). As a research project, TIRCON received funding in the 7th Framework Programme (FP7) of the European Union (EU) from 2011 to 2015. After successful and timely completion of the initial FP7 project, funding and donations from industry and patient organizations have sustained the further development of TIRCON's dedicated clinical research infrastructure and its governance architecture, as well as the ongoing efforts undertaken in the NBIA community to establish a network of care. From the beginning, the University Hospital of the Ludwig-Maximilians-University in Munich, Germany has been coordinating the TIRCON initiative. It consists of 8 work packages, of which the first double-blind, placebo-controlled, randomized, multi-site clinical trial in NBIA (deferiprone in PKAN, completed) and a global patient registry and biobank, currently comprising baseline and follow-up data of > 400 NBIA patients have gained particular importance. Here we describe TIRCON's history with all the challenges and achievements in diagnosing and treating NBIA. Today, TIRCON lays the ground for future clinical care and research. In these times, it may also serve as a good example of well-directed governmental funding and fruitful international scientific collaboration.
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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 | 0.030 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".