A retrospective observational analysis of the real-world care pathway of people with hereditary transthyretin amyloidosis with polyneuropathy in Italy
Bibliographic record
Abstract
BACKGROUND: This retrospective observational study described the epidemiology and the burden on the Italian healthcare service (SSN) of patients with polyneuropathy (PN) associated with hereditary transthyretin amyloidosis (ATTRv). RESEARCH DESIGN AND METHODS: From the Fondazione ReS (Ricerca e Salute) administrative healthcare database (~5.5 million inhabitants in 2021), patients were identified as having ATTRv-PN in 2021 if they had received treatments for ATTRv-PN under SSN reimbursement (i.e. tafamidis, patisiran, or inotersen) from 1 January 2014 to 31 December 2021 (index date in 2021). Demographics and comorbidities at the baseline, healthcare resource consumption, and related direct costs reimbursed by the SSN throughout the one-year follow-up were described. RESULTS: In 2021, 36 patients with ATTRv-PN (prevalence: 7.4/1,000,000) were identified (males were 83.3%; patients with ≥2 comorbidities were 61.1%; the mean age was 73 ± 8 years). During follow-up, of patients, 91.7% received drugs for ATTRv-PN; >50% received antiepileptics and acid suppressants; 22.2% were admitted to overnight hospitalizations; 30.6% accessed the emergency department; 97.2% received local outpatient specialist care. The per patient mean annual cost was € 122,017; drugs for ATTRv-PN accounted for 94.7% of the total expenditure. CONCLUSIONS: This study of real-world patients with ATTRv-PN showed a high rate of comorbidities, and substantial direct healthcare and economic burdens on the SSN.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".