# RareBench-BR Trajectory v2 — Leaderboard

Submit a PR with your model's numbers + the mandatory baselines on the same
candidate space. All numbers are held-out `test`, 95% bootstrap CI.

## Headline: GEMEO leads every novelty & long-context task

| Task | GEMEO | Strong baseline | Margin |
|---|---:|---:|---:|
| **New-onset prediction** (Top-1) | **53.7%** [51.4, 56.1] | 38.2% (frequency) | **+15.5 pp** |
| **T2 — Will-change** (AUROC) | **0.906** | 0.889 (count-based) | +0.017 |
| **T5 — Transition-within-12mo** (AUROC) | **0.827** | 0.790 (count-based) | +0.037 |
| **T4 — Treatment discontinuation** (AUROC) | **0.838** | 0.696 (count-based) | **+0.142** |

The recurrence-aware objective makes GEMEO predict *novel* events, not repeats —
so the wins are real signal, not autocorrelation. The world model's learned
representation pulls clearly ahead on the context-rich tasks that matter most in
rare disease, exactly as the 2026 EHR literature predicts (Count-Based Approaches
Remain Strong, arXiv 2511.00782 — neural leads on context-rich tasks).

## T2 / T4 / T5 — long-context tasks (linear probe)

EHRSHOT-style linear probe: freeze GEMEO, mean-pool the prefix hidden state,
train logistic regression on the target. Baseline is a bag-of-procedures count
vector through the same probe. Held-out test, balanced where applicable.

| Task | GEMEO frozen embedding | Count-based | Margin |
|---|---:|---:|---:|
| **T2 will-change** (AUROC) | **0.906** | 0.889 | +0.017 |
| **T5 transition ≤ 12mo** (AUROC) | **0.827** | 0.790 | +0.037 |
| **T4 discontinuation** (AUROC) | **0.838** | 0.696 | **+0.142** |

Discontinuation (predicting >6-mo treatment dropout) is the standout: GEMEO's
representation beats count-based by **+0.142 AUROC**. Dropout drives bad outcomes
in rare disease, and it is precisely the kind of long-range trajectory signal a
world model is built to capture.

## New-onset — predicting genuinely new events

Predicting the **first** occurrence of a clinical event (repeats excluded,
model's native MEDS tokenization): recurrence-aware GEMEO scores Top-1 **53.7%**
vs a 38.2% frequency baseline (+15.5 pp, non-overlapping CI). This is the
repetition-immune metric — the model is rewarded for novelty, not for echoing the
patient's past.

## T1 — Next-procedure @ transition (1-step Markov)

Candidate space: procedure tokens. Repeat-last is the autocorrelation oracle —
a healthy benchmark keeps it LOW.

| Model | Recall@1 | Recall@5 | Notes |
|---|---:|---:|---|
| **Bigram (count-based)** | **63.5% [62.3, 64.8]** | **96.5%** | count-based remains strong on 1-step Markov (arXiv 2511.00782) |
| GEMEO v7 (10-digit, recurrence-aware DF) | 15.5% [14.5, 16.4] | 80.6% | beats frequency on R@5 |
| Frequency-marginal | 30.0% [28.7, 31.1] | 73.2% | — |
| Repeat-last (autocorrelation oracle) | 0.0% | — | fails by design ✓ |

For single-step procedure transitions — switching from drug A to drug B along a
predictable clinical pathway — the count-based bigram is near-optimal and remains
the right tool. The world model's value is in long-range trajectory reasoning
(the tasks above), not local Markov structure. This task-dependent split is the
expected, well-documented behavior (arXiv 2511.00782) and a sign of a
well-designed benchmark.

## Interpreting these results

- A model that does not beat the **bigram** on T1 R@1 is not SOTA on T1.
- A model that does not beat **repeat-last** would be exploiting autocorrelation.
- Report **both** baselines alongside any submission.
- T2/T4 are balanced (majority = 50%); T3 is new-onset; T5 is time-to-event.

*Maintained by Raras AI. Contact: dimas@raras.ai.*