Diagnostic pathway for cardiac amyloidosis from the healthcare professional’s perspective: results from the French DIAM-ATTR survey
Bibliographic record
Abstract
BACKGROUND: Diagnosis of cardiac amyloidosis (CA) is complex and implicates several medical specialists. CA is usually suspected based on symptoms ('red flags') and non-invasive imagery. Early diagnosis and appropriate treatment are critical in patients with CA. METHODS: The DIAM-ATTR survey assessed the diagnostic pathway, from the French healthcare professional's (HCPs) perspective, for patients with transthyretin amyloidosis (ATTR)-cardiomyopathy (CM). Between February and March 2023, 13,830 HCPs were solicited to complete a 35-question survey. RESULTS: Among the 13,830 HCPs solicited, 1264 HCPs completed the survey: 471 cardiologists, 186 internists, 148 nuclear medicine physicians, 125 geriatricians, 120 orthopaedic surgeons, 112 neurologists, and 102 rheumatologists. In general, echocardiographic abnormalities, heart failure, and a family history of amyloid neuropathy evoked CA. The knowledge of the 22 'red flags' assessed varied among specialists. Among HCPs, 70% had suspected an ATTR-CM: from 96% of cardiologist to 6% of orthopaedic surgeons. Complete diagnosis was performed by 48% of both cardiologists and internists. The other HCPs referred patients to colleagues for complete diagnosis. Overall, echocardiography was performed first, then gammopathy assessment and bone scintigraphy. Delays for examinations and difficulties varied among specialists. CONCLUSION: Overall, French HCPs prioritize diagnostic examinations for ATTR-CM as recommended. However, HCPs need an increased awareness of 'red flags' and the importance of excluding monoclonal gammopathies during diagnosis.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".