ArticleNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2026
The role of "red flags" in the diagnostic work-up of hereditary transthyretin amyloidosis: a study using a machine-learning approach.
Article in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionHereditary transthyretin amyloidosis (ATTRv) is a rare progressive, potentially life-threatening multisystem disorder caused by mutations in the transthyretin (TTR) gene, with variable penetrance and heterogeneous phenotypes, often leading to diagnostic delays, particularly in non-endemic regions. Identifying clinical "red flags" is crucial to shorten diagnostic latency. Machine learning (ML), a branch of artificial intelligence (AI), together with explainable artificial intelligence (XAI), offers novel opportunities to refine diagnostic algorithms and prioritize predictive features in ATTRv. MATERIALS AND
methodsA total of 452 patients who underwent TTR genetic testing between 2019 and 2024 in Sicily were retrospectively analyzed. Genetic testing was performed via polymerase chain reaction (PCR) and sequencing of TTR exons 2-4. Patients were stratified into Western and Eastern Sicily sub-cohorts to train and validate supervised ML models. Multiple algorithms were compared with hyperparameter tuning via GridSearch with cross-validation. Model interpretability was ensured using SHapley Additive exPlanations (SHAP) values and permutation importance.
resultsAmong 452 patients, 68 (15%) carried a TTR mutation, 51.5% of whom were symptomatic. The most frequent red flags were sensory neuropathy (62.6%) and family history of cardiomyopathy (51.8%). Tree-based models outperformed other algorithms, with Random Forest selected for its optimal balance between precision and recall. Bilateral carpal tunnel syndrome, family history of neuropathy, and ataxia emerged as the most informative predictors. DISCUSSION: These findings suggest that integrating ML with clinical red flags may support the diagnostic decision-making process in patients referred for suspected ATTRv. However, these results should be considered exploratory and require validation in independent cohorts before clinical implementation.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.