CrossGAT Trial Eligibility — full-data model
The deployment checkpoint: a 3-class eligibility model that reranks clinical-trial retrieval candidates, refit on every labelled pair after cross-validation.
This is the standard final step after CV — cross-validation validated the procedure and its hyperparameters, and the shipped model applies them to all the data. If you want to reproduce reported numbers, or to ensemble, use the 5-fold CV checkpoints instead.
⚠️ No performance number can be reported for this checkpoint
It has seen every labelled pair in the collection, so it has no held-out data left and no unbiased performance estimate. Any number you compute on TREC CT 2021–23 with this model is contaminated.
Cite the cross-validated estimate instead, from the CV repo: graded NDCG@10 0.5973 → 0.6515 (+0.0542, p<0.0001, 162 topics) on hybrid BM25+gemma-med at the recommended operating point. That is the expected performance of this procedure; this checkpoint is the procedure applied to more data.
⚠️ This model is not self-contained
It takes parsed eligibility graphs, not raw text. Producing them needs the generator from the project repo plus an LLM endpoint (~38 GB VRAM). The graphs for all 13,229 TREC CT trials are published separately — clinical-trials-eligibility-graphs-rerank — so reproduction needs no LLM.
Recommended configuration
score = (1 - w) * retrieval_norm + w * p_eligible_norm with w = 0.5
Both terms min-max normalised within a topic. Never replace the first stage outright (w = 1.0): that loses significance on both retrievers tested. The model reranks; it cannot retrieve — per-topic AUC for relevant-vs-rest is 0.533, chance.
How the stopping point was chosen
With no validation split there is nothing to select on, so the epoch count was
fixed in advance from the CV runs. Averaging the per-fold validation curves
(not their argmaxes, which are noise around a plateau) puts val_ndcg10's optimum
at epoch 3:
| epoch | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
|---|---|---|---|---|---|---|---|---|---|
| mean val_ndcg10 | .602 | .623 | .615 | .624 | .623 | .616 | .611 | .600 | .607 |
The curve is flat from epoch 1 to 5, so the exact choice inside that band is not critical — which is the point. The median of the per-fold argmaxes would have said epoch 2, but that averages noise rather than signal.
Refit epochs carry ~26% more gradient steps than a CV fold (1,633 vs ~1,294 batches), so epoch 3 here is ≈3.8 CV-equivalent epochs — still inside the plateau.
Usage
pip install "crossgat @ git+https://github.com/JDev2001/msc_v2"
from crossgat_hf import load, score, rerank_score
pipe = load("2001jdev/crossgat-trial-eligibility-full") # fold=None -> model.ckpt
out = score(pipe, patient_graph, inc_graph, exc_graph,
patient_text="65yo male, type 2 diabetes, HbA1c 8.1% ...",
trial_text="Metformin in adults with type 2 diabetes ...")
out["class_probs"] # {'irrelevant': .., 'excluded': .., 'eligible': ..}
rerank_score(out) # p_eligible — fuse this with your retrieval score
patient_text and trial_text are required: the text branch is part of the
model, and omitting them raises rather than silently scoring an all-zero branch.
Training
- Data: all 78,370 labelled pairs (73,063 from TREC CT 2021–23 qrels + 5,307 hard negatives)
- Objective: cross-entropy + LambdaRank (k=10) on topic-grouped batches (4 topics/batch)
- Hard negatives: 40/topic from the retrieval candidate distribution, mined from hybrid gemma-med run files
- Text branch:
abhinand/MedEmbed-base-v0.1 - Epochs: 4 (0–3), fixed in advance; no early stopping, no checkpoint selection
Limitations
- No unbiased performance estimate — see the warning above.
- The first stage is the ceiling. A better retriever buys more than more reranker tuning.
- The hard-negative gain is retriever-sensitive (+0.0175 on gemma-med, +0.0148 on medembed): the negatives were mined from gemma-med candidates and transfer only partly.
- Within-collection evidence only. One corpus, one set of assessors.
- Not a clinical device. Research artefact. It does not determine patient eligibility and must not be used to make enrolment decisions.
Citation & provenance
Trained on TREC Clinical Trials track data (2021–23). Users must comply with the TREC data usage agreements for the underlying collection and relevance judgments.
Checkpoint SHA-256 prefix in checkpoint_manifest.json.