Moving towards establishing centres of excellence in cardiac amyloidosis: an International Cardio-Oncology Society statement
Bibliographic record
Abstract
The prevalence of amyloidosis has been increasing, driven by a combination of improved awareness, evolution of diagnostic pathways, and effective treatment options for both transthyretin and light chain amyloidosis. Due to the complexity of amyloidosis, centralised expert providers with experience in delineating the nuances of confirmatory diagnosis and management may be beneficial. There are many potential benefits of a centre of excellence designation for the treatment of amyloidosis including recognition of institutions that have been leading the way for the optimal treatment of this condition, establishing the expectations for any centre who is engaging in the treatment of amyloidosis and developing cooperative groups to allow more effective research in this disease space. Standardising the expectations and criteria for these centres is essential for ensuring the highest quality of clinical care and community education. In order to define what components are necessary for an effective centre of excellence for the treatment of amyloidosis, we prepared a survey in cooperation with a multidisciplinary panel of amyloidosis experts representing an international consortium. The purpose of this position statement is to identify the essential elements necessary for highly effective clinical care and to develop a general standard with which practices or institutions could be recognised as a centre of excellence.
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 | 0.114 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.006 | 0.029 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".