CIViC evidence levels used as the reference labels by Lin et al. 2025.
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Release 2026-10-09-ba02f2f4a36e · Evidence verified: Not verified
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4 evaluations · 4 results. Different protocols are not a single leaderboard.
Applied filters: All linked evaluations
| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Configuration: GPT-4o, basic prompt, temperature 1.0 (default) (Lin et al. 2025) | Protocol: Lin et al. 2025 CIViC evidence-level assignment Dataset: CIViC clinical evidence summary, 4,426 variant-disease associations (accessed 2024-11-20) | 0.186 top-1-accuracy fraction · higher Uncertainty: 95% CI 0.1857 to 0.1874 Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourcegpt-4o-basic on civic (Lin et al. 2025) egfrnsclc-20261009-protocol-lin2025-civic-level-assignment Aggregation: Not reported Benchmarking large language models GPT-4o, llama 3.1, and qwen 2.5 for cancer genetic variant classification · Table 1 (Tab1), row 'CIViC', column 'GPT-4o' Mean accuracy; 95% CI in the next column |
| Configuration: Llama 3.1 70B, basic prompt, temperature 0.8 (default) (Lin et al. 2025) | Protocol: Lin et al. 2025 CIViC evidence-level assignment Dataset: CIViC clinical evidence summary, 4,426 variant-disease associations (accessed 2024-11-20) | 0.121 top-1-accuracy fraction · higher Uncertainty: 95% CI 0.1205 to 0.1219 Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourcellama-basic-t0-8 on civic (Lin et al. 2025) egfrnsclc-20261009-protocol-lin2025-civic-level-assignment Aggregation: Not reported Benchmarking large language models GPT-4o, llama 3.1, and qwen 2.5 for cancer genetic variant classification · Table 1 (Tab1), row 'CIViC', column 'Llama 3' Mean accuracy; 95% CI in the next column |
| Configuration: Qwen 2.5 72B, basic prompt, temperature 0.8 (default) (Lin et al. 2025) | Protocol: Lin et al. 2025 CIViC evidence-level assignment Dataset: CIViC clinical evidence summary, 4,426 variant-disease associations (accessed 2024-11-20) | 0.248 top-1-accuracy fraction · higher Uncertainty: 95% CI 0.2477 to 0.2492 Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceqwen-basic on civic (Lin et al. 2025) egfrnsclc-20261009-protocol-lin2025-civic-level-assignment Aggregation: Not reported Benchmarking large language models GPT-4o, llama 3.1, and qwen 2.5 for cancer genetic variant classification · Table 1 (Tab1), row 'CIViC', column 'Qwen 2.5' Mean accuracy; 95% CI in the next column |
| Configuration: Qwen 2.5 72B, refined prompt, temperature 0.8 (default) (Lin et al. 2025) | Protocol: Lin et al. 2025 CIViC evidence-level assignment Dataset: CIViC clinical evidence summary, 4,426 variant-disease associations (accessed 2024-11-20) | 0.172 top-1-accuracy fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceqwen-refined on civic (Lin et al. 2025) egfrnsclc-20261009-protocol-lin2025-civic-level-assignment Aggregation: Not reported Benchmarking large language models GPT-4o, llama 3.1, and qwen 2.5 for cancer genetic variant classification · Table 2 (Tab2), row 8 ('Qwen2.5', 'Refined prompt + Default temperature (0.8)', 'CIViC'), column 'Accuracy' |
Source checking is not independent reproduction. Release 2026-10-09-ba02f2f4a36e.
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Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.
One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.
6 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| attributes.population 4,426 associations: level A 166, B 1,465, C 1,547, D 1,216, E 32 Context-only references | Benchmarking large language models GPT-4o, llama 3.1, and qwen 2.5 for cancer genetic variant classification Lin et al. 2025 Methods P37 Version: npj Precision Oncology 9:141, published 2025-05-15; PMC12078457 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.source_locator Lin et al. 2025 Methods P37 Context-only references | Benchmarking large language models GPT-4o, llama 3.1, and qwen 2.5 for cancer genetic variant classification Lin et al. 2025 Methods P37 Version: npj Precision Oncology 9:141, published 2025-05-15; PMC12078457 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.total 4426 Context-only references | Benchmarking large language models GPT-4o, llama 3.1, and qwen 2.5 for cancer genetic variant classification Lin et al. 2025 Methods P37 Version: npj Precision Oncology 9:141, published 2025-05-15; PMC12078457 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.version CIViC Clinical Evidence Summary download, last accessed 2024-11-20 Context-only references | Benchmarking large language models GPT-4o, llama 3.1, and qwen 2.5 for cancer genetic variant classification Lin et al. 2025 Methods P37 Version: npj Precision Oncology 9:141, published 2025-05-15; PMC12078457 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| description CIViC evidence levels used as the reference labels by Lin et al. 2025. Context-only references | Benchmarking large language models GPT-4o, llama 3.1, and qwen 2.5 for cancer genetic variant classification Lin et al. 2025 Methods P37 Version: npj Precision Oncology 9:141, published 2025-05-15; PMC12078457 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| name CIViC clinical evidence summary, 4,426 variant-disease associations (accessed 2024-11-20) Context-only references | Benchmarking large language models GPT-4o, llama 3.1, and qwen 2.5 for cancer genetic variant classification Lin et al. 2025 Methods P37 Version: npj Precision Oncology 9:141, published 2025-05-15; PMC12078457 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Release 2026-10-09-ba02f2f4a36e · Record review: source checked
Stable ID: egfrnsclc-20261009-data-lin2025-civic-associations