# Medical Outcomes

A 32B clinical forecaster that matches GPT-5 on ICU outcomes at a fraction of the size

2.7× better calibrated than GPT-5 (ECE 0.031 vs. 0.084)

17% lower Brier score than the clinical base rate

## What we did

- Built ICU patient trajectories from MIMIC-III — vitals, lab panels, clinical notes, medications.
- Anchored forecasting questions at each point in the ICU stay (discharge, deterioration, escalation).
- Labeled each question using the actual downstream outcome recorded in the EHR.
- Trained a 32B model and benchmarked against GPT-5 and gpt-oss-120B.

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## Example datapoint

A sample training example — question, source, and outcome-derived label.

### DATASET ICU trajectories

#### Question

Given the first 24 hours of ICU admission (vitals, labs, initial note, ventilator settings at hour 18), will this patient die before discharge?

#### Question source

MIMIC-III admission note ICU hour 24

Septic shock on presentation; lactate 4.2, norepi 0.18 mcg/kg/min

#### Label

No.

#### Type

binary

#### Confidence

0.81

#### Label source

Discharge summary ICU day 11

Extubated day 6; discharged to ward in stable condition

### DATASET ICU trajectories

#### Question

What is the probability this patient is discharged alive within 72 hours given their current hour-48 trajectory (SOFA 3, off pressors, weaning vent)?

#### Question source

Nursing flowsheet ICU hour 48

SOFA dropped from 9 to 3; SBT passed this morning

#### Label

0.64

#### Type

continuous

#### Confidence

0.78

#### Label source

EHR discharge event ICU hour 66

Transferred to step-down unit; ICU discharge confirmed

### DATASET ICU trajectories

#### Question

Will this ICU patient require renal replacement therapy within 48 hours given hour-12 Cr trajectory (1.4 → 2.1) and urine output <0.3 mL/kg/hr for the last 6 hours?

#### Question source

Lab panel ICU hour 12

KDIGO stage 2 AKI; nephrology consulted

#### Label

Yes.

#### Type

binary

#### Confidence

0.83

#### Label source

Procedure log ICU hour 42

CVVHDF initiated; volume-overloaded and hyperkalemic

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## Results

Benchmark comparisons against frontier models.

### ICU Outcome Prediction: Brier and Calibration Error

Our 32B trained model matches GPT-5 on Brier (0.149 vs. 0.148) while achieving 2.7× better calibration (ECE 0.031 vs. 0.084) — and it beats the clinical base rate (0.149 vs. 0.180). gpt-oss-120B trails both on every metric. Initial results; more to come as we publish the case study.

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