The impact of clinical heterogeneity on conducting network meta-analyses in transthyretin amyloidosis with polyneuropathy
Bibliographic record
Abstract
Objective: The comparative safety and efficacy of tafamidis, patisiran and inotersen treatments for transthyretin amyloidosis with polyneuropathy (ATTR-PN) has not been evaluated in clinical trials. In the absence of head-to-head evidence, indirect treatment comparisons such as network meta-analyses (NMAs) can be performed to evaluate relative effects of treatments. This study aims to assess the feasibility of conducting an NMA of available therapies for ATTR-PN patients.Methods: Pivotal trials for three approved ATTR-PN treatments, tafamidis (Fx-005), patisiran (APOLLO) and inotersen (NEURO-TTR), were compared in terms of study design, baseline population characteristics, outcome definitions and baseline risk. These assessments of heterogeneity informed the decision to perform Bayesian NMAs.Results: Despite similar study designs, clear differences in eligibility criteria between trials were accompanied by imbalances in baseline population characteristics considered to be plausible effect modifiers, such as disease stage and previous treatment. Of the outcomes assessed, only quality of life and adverse events were similarly reported in all trials. Neuropathy outcomes were not evaluated consistently between trials.Conclusions: An NMA of ATTR-PN treatments was not feasible, given the observed cross-trial heterogeneity. This decision highlights the importance of careful consideration for clinical heterogeneity that may threaten the validity of indirect comparisons.
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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 | 0.377 | 0.547 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.026 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".