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Senior Technical Consultant, NHS NSS) /E (Lisa Hopcroft - Senior Technical Consultant, NHS NSS) /Pg 7 0 R /K [34] >> endobj 21 0 obj << /Type /StructElem /S /H6 /P 111 0 R /T (Simon Rogers - Senior Technical Consultant, NHS NSS) /E (Simon Rogers - Senior Technical Consultant, NHS NSS) /Pg 7 0 R /K [35] >> endobj 22 0 obj << /Type /StructElem /S /Figure /P 13 0 R /Pg 7 0 R /K [54] >> endobj 23 0 obj << /Type /StructElem /S /Figure /P 13 0 R /Pg 7 0 R /K [55] >> endobj 24 0 obj << /Type /StructElem /S /H2 /T (BACKGROUND) /E (BACKGROUND) /Pg 7 0 R /K [16] >> endobj 25 0 obj << /Type /StructElem /S /H6 /P 112 0 R /T /E /Pg 7 0 R /K [17] >> endobj 26 0 obj << /Type /StructElem /S /H6 /P 113 0 R /T (During COVID, the urgent need to locate ventilators showed the importance of a national harmonised database.) /E (During COVID, the urgent need to locate ventilators showed the importance of a national harmonised database.) /Pg 7 0 R /K [18] >> endobj 27 0 obj << /Type /StructElem /S /H6 /P 114 0 R /T (AI, particularly large language models \(LLMs\), can support the unification of local names under a single national standard.) /E (AI, particularly large language models \(LLMs\), can support the unification of local names under a single national standard.) /Pg 7 0 R /K [19] >> endobj 28 0 obj << /Type /StructElem /S /H2 /T (METHODOLOGY) /E (METHODOLOGY) /Pg 7 0 R /K [20] >> endobj 29 0 obj << /Type /StructElem /S /P /E (REQUIREMENTS AND DATA COLLECTION) /Pg 7 0 R /K [2] >> endobj 30 0 obj << /Type /StructElem /S /Figure /P 13 0 R /Pg 7 0 R /K [56] >> endobj 31 0 obj << /Type /StructElem /S /P /E (SYNTHETIC DATA GENERATION) /Pg 7 0 R /K [1] >> endobj 32 0 obj << /Type /StructElem /S /Figure /P 13 0 R /Pg 7 0 R /K [57] >> endobj 33 0 obj << /Type /StructElem /S /P /E (DATA PREPROCESSING) /Pg 7 0 R /K [6] >> endobj 34 0 obj << /Type /StructElem /S /Figure /P 13 0 R /Pg 7 0 R /K [58] >> endobj 35 0 obj << /Type /StructElem /S /Figure /P 13 0 R /Pg 7 0 R /K [59] >> endobj 36 0 obj << /Type /StructElem /S /P /E (BASELINE MODEL CREATION) /Pg 7 0 R /K [3] >> endobj 37 0 obj << /Type /StructElem /S /Figure /P 13 0 R /Pg 7 0 R /K [60] >> endobj 38 0 obj << /Type /StructElem /S /Figure /P 13 0 R /Pg 7 0 R /K [61] >> endobj 39 0 obj << /Type /StructElem /S /Figure /P 13 0 R /Pg 7 0 R /K [62] >> endobj 40 0 obj << /Type /StructElem /S /P /E (EVALUATION OF BASELINE) /Pg 7 0 R /K [5] >> endobj 41 0 obj << /Type /StructElem /S /P /E (FINE-TUNING & OPTIMISATION) /Pg 7 0 R /K [4] >> endobj 42 0 obj << /Type /StructElem /S /Figure /P 13 0 R /Pg 7 0 R /K [63] >> endobj 43 0 obj << /Type /StructElem /S /Figure /P 13 0 R /Pg 7 0 R /K [64] >> endobj 44 0 obj << /Type /StructElem /S /Figure /P 13 0 R /Pg 7 0 R /K [65] >> endobj 45 0 obj << /Type /StructElem /S /P /E (FINAL EVALUATION & COMPARISON) /Pg 7 0 R /K [0] >> endobj 46 0 obj << /Type /StructElem /S /Figure /P 13 0 R /Pg 7 0 R /K [66] >> endobj 47 0 obj << /Type /StructElem /S /H2 /T (OBJECTIVE) /E (OBJECTIVE) /Pg 7 0 R /K [10] >> endobj 48 0 obj << /Type /StructElem /S /H6 /P 115 0 R /T /E /Pg 7 0 R /K [11 12] >> endobj 49 0 obj << /Type /StructElem /S /H6 /P 116 0 R /T (Provide a confidence score for each match.) /E (Provide a confidence score for each match.) /Pg 7 0 R >> endobj 50 0 obj << /Type /StructElem /S /H6 /P 117 0 R /T (Benchmark against a manually curated gold-standard dataset.) /E (Benchmark against a manually curated gold-standard dataset.) /Pg 7 0 R /K [13] >> endobj 51 0 obj << /Type /StructElem /S /H6 /P 118 0 R /T (Fine-tune and optimise models for accuracy and reliability.) /E (Fine-tune and optimise models for accuracy and reliability.) /Pg 7 0 R /K [14] >> endobj 52 0 obj << /Type /StructElem /S /H6 /P 119 0 R /T (Compare different models to identify the best-performing approach.) /E (Compare different models to identify the best-performing approach.) /Pg 7 0 R /K [15] >> endobj 53 0 obj << /Type /StructElem /S /Figure /P 13 0 R /Pg 7 0 R /K [67] >> endobj 54 0 obj << /Type /StructElem /S /H2 /T (ANALYSIS) /E (ANALYSIS) /Pg 7 0 R /K [21] >> endobj 55 0 obj << /Type /StructElem /S /H3 /T (A Sentence Transformer based on BERT measured similarity between local and national equipment names, with standard BERT serving as the baseline. Dataset augmentation generated synthetic pairs by swapping and reordering words to expand coverage. Pre-processing, including punctuation removal, ensured clean embeddings. Fine-tuning adapted the model to the specific medical equipment context, improving accuracy, precision, and top-k recall. Evaluation metrics, including accuracy, precision, F1, and top-k recall, assessed prediction reliability and ranking quality.) /E (A Sentence Transformer based on BERT measured similarity between local and national equipment names, with standard BERT serving as the baseline. Dataset augmentation generated synthetic pairs by swapping and reordering words to expand coverage. Pre-processing, including punctuation removal, ensured clean embeddings. Fine-tuning adapted the model to the specific medical equipment context, improving accuracy, precision, and top-k recall. Evaluation metrics, including accuracy, precision, F1, and top-k recall, assessed prediction reliability and ranking quality.) /Pg 7 0 R /K [22] >> endobj 56 0 obj << /Type /StructElem /S /Figure /P 13 0 R /Pg 7 0 R /K [8] >> endobj 57 0 obj << /Type /StructElem /S /Figure /P 13 0 R /Pg 7 0 R /K [9] >> endobj 58 0 obj << /Type /StructElem /S /Figure /P 13 0 R /Pg 7 0 R /K [68] >> endobj 59 0 obj << /Type /StructElem /S /H4 /T (RESULTS) /E (RESULTS) /Pg 7 0 R /K [25] >> endobj 60 0 obj << /Type /StructElem /S /P /E (The fine-tuned model outperforms the baseline across most evaluation dimensions:) /Pg 7 0 R /K [26] >> endobj 61 0 obj << /Type /StructElem /S /P /P 120 0 R /E (Higher and more stable precision) /Pg 7 0 R /K [27] >> endobj 62 0 obj << /Type /StructElem /S /P /P 121 0 R /E (Maintained coverage until very strict thresholds) /Pg 7 0 R /K [28] >> endobj 63 0 obj << /Type /StructElem /S /P /P 122 0 R /E (Improved top-k retrieval performance) /Pg 7 0 R /K [29] >> endobj 64 0 obj << /Type /StructElem /S /H4 /T (CONCLUSION) /E (CONCLUSION) /Pg 7 0 R /K [23] >> endobj 65 0 obj << /Type /StructElem /S /P /E (The fine-tuned model demonstrates strong potential to standardise category mapping, reduce manual effort, and improve data quality across NHS health boards. 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