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| Format: | Recurso digital |
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Zenodo
2026
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| Accès en ligne: | https://doi.org/10.5281/zenodo.19548362 |
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Table des matières:
- <h2>What's changed</h2> <p>Revised manuscript accompanying the updated preprint (Zenodo DOI: 10.5281/zenodo.19477877).</p> <h3>Key changes from v1.0</h3> <ul> <li><strong>Title reframed</strong> around ablation and error decomposition findings (from generic "RAG improves...")</li> <li><strong>5 critical citations added</strong>: RDguru (Yang 2025), Reese et al. (2026), Chimirri et al. (2025), Zhong et al. (2025), DeepRare (Zhao et al. 2026, Nature)</li> <li><strong>Competitive landscape positioning</strong> with comparison table against prior systems</li> <li><strong>Reference numbering corrected</strong> to sequential first-appearance order (33 refs, up from 28)</li> <li><strong>Ablation results clarified</strong> with absolute case counts</li> <li><strong>Confidence calibration reconciled</strong> (93% on 70-case set; 90% on 40-case subset)</li> <li><strong>Affiliation corrected</strong> to Independent Researcher, Finley, NSW, Australia</li> </ul> <h3>Code and data</h3> <p>No changes to the replication code or benchmark data. All experimental results remain identical to v1.0.</p> <p><strong>Full citation:</strong></p> <blockquote> <p>Farquhar, H. (2026). Retrieval quality, not model capability, constrains RAG-assisted rare disease diagnosis: ablation and error analysis on 85 clinical vignettes. Zenodo. https://doi.org/10.5281/zenodo.19477877</p> </blockquote>