REFERENCES

1. Watson C. Hernia. In: Watson C, Davies J, Editors. Ellis and Calne’s lecture notes in general surgery. Hoboken: John Wiley & Son; 2023. pp. 311-22.

2. Le TN, Afshar Ali M, Gadzhanova S, et al. Hernia repair prevalence by age and gender among the Australian adult population from 2017 to 2021. Critical Public Health. 2024;34:1-11.

3. Mishali M, Sheffer N, Mishali O, Negev M. Understanding variation among medical device reporting sources: a study of the MAUDE database. Clin Ther. 2025;47:76-81.

4. Clavel M, Durán F, Eker S, et al. Maude manual (version 3.1). SRI International, 2020. Available from: https://gentoo.uls.co.za/distfiles/5d/Maude-3.1-manual.pdf [accessed 16 October 2025].

5. Kou Q, Wu M. Unlocking the potential of natural language processing in decoding medical device adverse events. In: Lane M, Sethumadhavan A, Editors. Collaborative intelligence: how humans and AI are transforming our world. Cambridge: MIT Press; 2024. pp. 197-211.

6. I. Natural language processing in medical science and healthcare. Medicon Med Sci. 2023:4;1-2.

7. Liao TJ, Crosby L, Cross K, Chen M, Elespuru R. Medical device report analyses from MAUDE: device and patient outcomes, adverse events, and sex-based differential effects. Regul Toxicol Pharmacol. 2024;149:105591.

8. Bala I, Malhotra A. Fuzzy classification with comprehensive learning gravitational search algorithm in breast tumor detection. IJRTE. 2019;8:2688-94.

9. Martin SC, Fitzgerald JJ. Tipu Zahed Aziz, MD (November 9, 1956-October 25, 2024). Neuromodulation. 2025;28:371-2.

10. Touvron H, Lavril T, Izacard G, et al. LLaMA: open and efficient foundation language models. arXiv 2023; arXiv:2302.13971. Available from https://doi.org/10.48550/arXiv.2302.13971 [accessed 16 October 2025].

11. Bumgardner VK, Larsen MA, Anderson MB, Sayre GG, Fecho K, Pfaff ER. Local large language models for complex structured medical tasks. arXiv 2023; arXiv:2308.01727. Available from https://doi.org/10.48550/arXiv.2308.01727 [accessed 16 October 2025].

12. Wang H, Gao C, Dantona C, Hull B, Sun J. DRG-LLaMA : tuning LLaMA model to predict diagnosis-related group for hospitalized patients. NPJ Digit Med. 2024;7:16.

13. Zhang R, Han J, Zhou A, et al. LLaMA-Adapter: efficient fine-tuning of large language models with zero-initialized attention. arXiv 2024; arXiv:2303.16199. Available from https://doi.org/10.48550/arXiv.2303.16199 [accessed 16 October 2025].

14. Frayling E, Lever J, McDonald G. Zero-shot and few-shot generation strategies for artificial clinical records. arXiv 2024; arXiv:2403.08664. Available from https://doi.org/10.48550/arXiv.2403.08664 [accessed 16 October 2025].

15. Lewis P, Perez E, Piktus A, et al. Retrieval-augmented generation for knowledge-intensive NLP tasks. arXiv 2020; arXiv:2005.11401. Available from https://doi.org/10.48550/arXiv.2005.11401 [accessed 16 October 2025].

16. Gao Y, Xiong Y, Gao X, et al. Retrieval-augmented generation for large language models: a survey. arXiv 2023; arXiv:2312.10997. Available from https://doi.org/10.48550/arXiv.2312.10997 [accessed 16 October 2025].

17. Salemi A, Zamani H. Evaluating retrieval quality in retrieval-augmented generation. In: SIGIR 2024: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval; 2024 Jul 14-18; Washington DC, USA. New York: Association for Computing Machinery; 2024. pp. 2395-400.

18. Yu H, Guo P, Sano A. Zero-shot ECG diagnosis with large language models and retrieval-augmented generation. In Machine learning for health (ML4H); 2023 Dec 10; New Orleans, USA. Cambridge: PMLR; 2023. pp. 650-63. Available from https://proceedings.mlr.press/v225/yu23b.html [accessed 16 October 2025].

19. Thompson WE, Vidmar DM, De Freitas JK, et al. Large language models with retrieval-augmented generation for zero-shot disease phenotyping. arXiv 2023; arXiv:2312.06457. Available from https://doi.org/10.48550/arXiv.2312.06457 [accessed 16 October 2025].

20. Mahbub S, Ellington C, Alinejad S, et al. From one to zero: RAG-IM adapts language models for interpretable zero-shot predictions on clinical tabular data. In: NeurIPS 2024 Third Table Representation Learning Workshop, 2024. Available from https://openreview.net/forum?id=3OYjWzqqC1 [accessed 16 October 2025].

21. Ke YH, Jin L, Elangovan K, et al. Retrieval augmented generation for 10 large language models and its generalizability in assessing medical fitness. NPJ Digit Med. 2025;8:187.

22. Dong X, Zhao D, Meng J, Guo B, Lin H. SyRACT: zero-shot biomedical document-level relation extraction with synergistic RAG and CoT. Bioinformatics. 2025;41:btaf356.

23. Mishali M, Sheffer N, Mishali O, Negev M. Evaluation of reporting trends in the MAUDE Database: 1991 to 2022. Digit Health. 2025;11:20552076251314094.

24. Bala I, Kelly T, Stanford T, Gillam MH, Mitchell L. Machine learning-based analysis of adverse events in mesh implant surgery reports. Soc Netw Anal Min. 2024;14:1229.

25. Boutin R, Bouveyron C, Latouche P. Embedded topics in the stochastic block model. Stat Comput. 2023;33:10265.

26. S SK, G GJWK, E GMK, J MR, A RGS, E Y. A RAG-based medical assistant especially for infectious diseases. In: 2024 International Conference on Inventive Computation Technologies (ICICT); 2024 Apr 24-26; Lalitpur, Nepal. New York: IEEE; 2024. pp. 1128-33.

27. Galli C, Donos N, Calciolari E. Performance of 4 pre-trained sentence transformer models in the semantic query of a systematic review dataset on peri-implantitis. Information. 2024;15:68.

28. Wang X, Han Y, Tang M, Zhang F. Robust orbital game policy in multiple disturbed environments: an approach based on causality diversity maximal marginal relevance algorithm. In: Liu L, Niu Y, Fu W, Qu Y, Editors. Proceedings of 4th 2024 International Conference on Autonomous Unmanned Systems (4th ICAUS 2024); 2024 Sep 19-21; Shenyang, China. Singapore: Springer; 2025. pp. 355-69.

29. Badshah S, Sajjad H. Quantifying the capabilities of LLMs across scale and precision. arXiv 2024; arXiv:2405.03146. Available from https://doi.org/10.48550/arXiv.2405.03146 [accessed 16 October 2025].

30. Verma V, Aggarwal RK. A comparative analysis of similarity measures akin to the Jaccard index in collaborative recommendations: empirical and theoretical perspective. Soc Netw Anal Min. 2020;10:660.

31. Bala I, Kelly T, Lim R, Gillam MH, Mitchell L. An effective approach for multiclass classification of adverse events using machine learning. JCCE. 2024;3:226-39.

32. Groves M, O'Rourke P, Alexander H. Clinical reasoning: the relative contribution of identification, interpretation and hypothesis errors to misdiagnosis. Med Teach. 2003;25:621-5.

33. McHugh ML. The chi-square test of independence. Biochem Med. 2013;23:143-9.

34. Bala I, Mitchell L, Gillam MH. Analysis of voluntarily reported data post mesh implantation for detecting public emotion and identifying concern reports. arXiv 2025; arXiv:2509.04517. Available from https://doi.org/10.48550/arXiv.2509.04517 [accessed 16 October 2025].