---
license: other
language: en
tags:
  - medical
  - triage
  - emergency-medicine
  - esi
  - bert
  - research-only
  - not-for-clinical-use
base_model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
---

# BERT ESI Triage — v50 (RESEARCH CHECKPOINT)

> # ⚠️ RESEARCH USE ONLY — NOT FOR CLINICAL DECISIONS
>
> **NOT A MEDICAL DEVICE.** This model is an INTERMEDIATE training checkpoint
> released for research transparency and version-iteration history. It is NOT
> approved for clinical use and contains DOCUMENTED REGRESSIONS vs the
> production model (v49). Use v49 if you need the higher-quality release.
>
> **Do not enter real patient data.**

## What this is

`bert-esi-triage-v50` is a 110M-parameter BiomedBERT classifier fine-tuned
to assign Emergency Severity Index (ESI 1–5) acuity from triage-time chief
complaint, vitals, and demographics. It is an INTERMEDIATE checkpoint in
our v49 → v50 → v52 iteration history. v50 was trained on a corpus
addressing several v49 gaps (PMH dilution, ESI 3 boundary, gender atypical
MI) but introduced new regressions documented below.

**For production use, please use v49:** `vadimbelsky/bert-esi-triage-v49`.

## Why this exists

The medical-triage research community benefits from seeing intermediate
training checkpoints with HONEST scorecards. v50 represents:
- Validated improvements on narrative dialects (ER-REASON +10.5pp,
  PMH dilution +15.4pp, gender atypical MI +29pp)
- Validated regressions on compact-CC dialects (MC-MED ESI 1 R -24pp,
  adversarial ESI 1 R -27pp)
- A data point for understanding the trade-off between narrative-
  generalization and compact-CC-specialization

This release is intended for researchers studying:
- Training corpus impact on model generalization
- Fairness regression mechanisms (race-driven prediction flips
  doubled vs v49)
- Engine-ensemble mitigation strategies (engine cuts v50's 26%
  fairness swings back to 0%)

## v50 vs v49 Honest Scorecard

### Improvements

| Slice | v49 | v50 | Δ |
|---|---|---|---|
| MIMIC exact | 54.7% | 56.7% | **+2.0** |
| MC-MED exact | 58.9% | 61.7% | +2.8 |
| OB emergency exact | 65.3% | 68.4% | +3.1 |
| OB emergency ESI 1 R | 85.0% | 91.7% | +6.7 |
| Anaphylaxis ESI 1 R | 94.1% | 97.1% | +3.0 |
| ER-REASON narrative | 44.5% | 55.0% | **+10.5** |
| Gender atypical MI synth | 52.0% | 81.0% | **+29.0** |
| PMH dilution synth | 23.1% | 38.5% | **+15.4** |
| ESI 3 boundary synth | 44.7% | 61.7% | **+17.0** |

### Regressions

| Slice | v49 | v50 | Δ |
|---|---|---|---|
| **MC-MED ESI 1 recall** | **89.0%** | **65.0%** | **-24.0** ⚠️ |
| Mimic ESI 1 R | 84.6% | 75.6% | **-9.1** |
| Adversarial ESI 1 R | 93.3% | 66.7% | **-26.7** ⚠️ |
| Stroke exact | 58.8% | 50.5% | -8.3 |
| Sepsis exact | 62.4% | 55.9% | -6.5 |
| Sepsis ESI 1 R | 97.5% | 95.0% | -2.5 |
| Stroke ESI 1 R | 85.0% | 80.0% | -5.0 |
| Lukina exact | 49.3% | 46.3% | -3.0 |
| MIETIC exact | 83.5% | 80.0% | -3.5 |
| SES fairness slice | 55.0% | 26.0% | **-29.0** |
| SCD fairness slice | 94.0% | 80.0% | -14.0 |
| Pediatric carved ESI 1 R | 100.0% | 87.5% | -12.5 |
| Gender MI fairness | 45.0% | 32.0% | -13.0 |

### Counterfactual Fairness (Race-Demographic Invariance)

200 paired records (50 pair_ids × 4 demographic variants):

| Metric | v49 | v50 | Δ |
|---|---|---|---|
| Pair-mean max ESI Δ | 0.42 | **0.92** | **+0.50** ⚠️ |
| Pairs perfectly invariant | 72% | 54% | -18 |
| **Pairs with ESI swing ≥ 2** | **12%** | **26%** | **+14pp** ⚠️ |
| Race-driven swing pairs | 8 | 15 | +7 |
| Race mean swing | 1.50 | 1.67 | +0.17 |

**The engine ensemble (`min(BERT, engine_v4)`) reduces v50's fairness
regression to 0% ≥2 swing — engine is REQUIRED if v50 is used at all.**

## Aggregated head-to-head

- **11 improvements** (>+1pp)
- **18 regressions** (<-1pp)
- 5 stable (±1pp)
- **NET: -2.5pp exact, -7.2pp ESI 1 recall vs v49**

## What this model is good for

- **Research only**: studying corpus-balance trade-offs
- **Ablation reference**: documenting what gains came from v50 corpus
  additions (gender atypical MI synth, PMH dilution synth, ESI 3 boundary)
- **Engineering validation**: testing that engine ensemble can rescue
  a fairness-regressed BERT

## What this model is NOT good for

- **Clinical decisions** — UNSAFE due to MC-MED ESI 1 R regression
- **Production triage** — v49 outperforms it on safety metrics
- **Stanford-style telegraphic input** — 24pp ESI 1 catch loss
- **Adversarial inputs** — 27pp ESI 1 catch loss on adversarial probe

## Architecture

Identical to v49: BiomedBERT-base + 21-head V44MultiHeadBERT.

```python
import torch
from transformers import AutoTokenizer

# Load weights
state = torch.load("model.pt", map_location="cpu")
# Model architecture is V44MultiHeadBERT from train_bert_v44_bf16.py
```

See the v49 repository for full inference code:
`vadimbelsky/bert-esi-triage-v49`.

## Training details

- Base: BiomedBERT-base (~110M params)
- Heads: 21 (esi_head + symptom_head + 19 perception/safety heads)
- Training: 3 epochs, bf16, batch 64, lr 3e-5
- Corpus: ~382K records (v50 corpus, predecessor of v52 tiered Pillar 4 corpus)
- Resume strategy: continued from v50 epoch 2 best (epoch 3 finalized
  symptom_f1_micro = 0.366)
- val esi_exact at end of training: 76.9%
- val esi_adjacent at end of training: 97.9%

## What v50 taught us (used to design v52)

1. Tiered Pillar 4 weighting > uniform (proves 17% of "label_noise"
   were ambiguous, not noisy)
2. Symptom-head extraction must be re-run on all synth seeds
3. ESI 1 archetype synth must NOT be added without engine extraction
4. MC-MED ESI 1 corpus balance is critical — Pillar 1 fix in v52
5. Fairness regression must be measured via paired counterfactual records

These lessons are baked into v52 (training now, ETA 2.5h from v50 launch).

## Maintainer

vadim.belsky@gmail.com

## Citation

```
@misc{belsky2026esi_v50,
  title={BERT ESI Triage v50: Intermediate Research Checkpoint},
  author={Belsky, Vadim},
  year={2026},
  publisher={HuggingFace}
  note={Research checkpoint with documented regressions vs v49 production}
}
```

## Files in this repository

- `model.pt` — full model state dict (438 MB)
- `README.md` — this file