Fine-tuned MedCPT cross-encoder (passage retrieval)
MedCPT cross-encoder fine-tuned on the CIViC-Fact training data; used downstream for the post-cutoff retrieval evaluation for its lower resource requirement (single-GPU, non-generative).
Overview
MedCPT cross-encoder fine-tuned on the CIViC-Fact training data; used downstream for the post-cutoff retrieval evaluation for its lower resource requirement (single-GPU, non-generative).
Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.
Evaluations and results
1 evaluation · 1 result. Different protocols are not a single leaderboard.
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| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Configuration: Fine-tuned MedCPT cross-encoder (passage retrieval) | Protocol: CIViC-Fact v3 within-linked-publication passage retrieval, post-cutoff cohort Dataset: CIViC-Fact v3 post-cutoff temporal-evaluation cohort, 2026-03-03 to 2026-06-09 | 37/40 (92.5%) appropriate_retrieval_rate_postcutoff percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCIViC-Fact v3 post-cutoff within-linked-publication retrieval: fine-tuned MedCPT cross-encoder Not reported Aggregation: Not reported CIViC-Fact: a proof-of-concept framework for AI-assisted verification of cancer variant interpretations (bioRxiv v3) · Results, 'Passage Retrieval Models Perform Well without Fine-Tuning' subsection (printed page 14 / PDF page 15): '...both the fine-tuned MedCPT model and the 8B Qwen 3 reranker performed similarly retrieving appropriate content for 37 of the remaining 40 entries (92.5%).' |
Source checking is not independent reproduction. Release 2026-10-07-1448159e6a81.
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Evidence
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Sources and history
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Release 2026-10-07-1448159e6a81 · Record review: source checked
1 source records and release history
- CIViC-Fact: a proof-of-concept framework for AI-assisted verification of cancer variant interpretations (bioRxiv v3) · Original source · bioRxiv preprint doi:10.1101/2025.09.10.675443, Version 3, posted 2026-08-29 (per in-document header and bioRxiv API version metadata); published: NA.
Technical metadata and extraction receipts
Stable ID: ucc-clinical-egfr-config-medcpt-ft
- areas
- dna-genomes
- contexts
- clinical_research
- review
- method: automated_source_review; reviewer: Claude Sonnet EGFR-evidence-research worker; date: 2026-10-07; note: Source-backed literature curation with independent automated transcription and scope checks, independently reviewed by Codex across two review cycles before ingestion. No new model execution, independent experimental replication, qualified human scientific review or clinical validation.; source id: ucc-clinical-egfr-source-civicfact-v3
- model version
- ncbi/MedCPT-Cross-Encoder (Huggingface model identifier, confirmed Table 4), fine-tuned on the CIViC-Fact training partition
- model identity note
- 'ncbi/MedCPT-Cross-Encoder' is the base Huggingface model namespace/identifier as printed in Table 4. This is the identifier only, not an immutable checkpoint revision (e.g. a pinned commit hash or snapshot date); the source does not report one for this model, and no new model execution is implied.
- fine tuning
- loss: Cached Multiple Negatives Ranking; effective batch size: 64; learning rate: 0.00002; max epochs: 5; early stopping: true; hardware: A6000 GPU; dev metric: ndcg@10; note: Parameters as directly stated in Supplementary Methods, 'Fine-tuning passage retrieval models'. No seed, run count, checkpoint identity, or calibration procedure is reported there and none is assumed here.
- parent identity review
- source id: ucc-clinical-egfr-source-civicfact-v3; source locator: Table 4 (Huggingface ID / Model Version column); Supplementary Methods, 'Fine-tuning passage retrieval models'; Results, 'Passage Retrieval Models Perform Well without Fine-Tuning'.; method: automated_source_review