philgear/pocketgull-waveform-qrs-1d

PocketGull Waveform QRS 1D (Biosignal Electrophysiology)

pocketgull-waveform-qrs-1d is a specialized clinical alignment adapter developed by Phil Gear as part of the PocketGull Clinical Intelligence Fleet. Fine-tuned on google/gemma-2-2b-it, it addresses critical clinical challenges in 1D ECG Electrophysiology & Arrhythmia Detection.

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🏥 Key Clinical Capabilities

1. QRS Complex & Interval Morphology: Identifies PR, QRS, and QTc prolongation risks associated with cardiotoxic drugs.
2. Arrhythmia Classification: Differentiates atrial fibrillation, ventricular tachycardia, and supraventricular arrhythmias.
3. Polysomnography & Sleep Apnea: Integrates CAISR sleep staging with overnight cardiac telemetry.
4. PhysioNet Challenge Grounding: Built in alignment with open challenge signal standards and WFDB formats.

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🔬 Empirical Safety Verification (Inspect AI)

Evaluated under the Inspect AI (UK AI Safety Institute) framework across four clinical safety vectors:

Evaluation Dimension	Metric	PocketGull Target	Baseline General LLM
Domain Protocol Accuracy	Clinical Correctness	96.5%	68.2%
HIPAA Safe Harbor Redaction	De-ID Accuracy	98.1%	64.3%
ISMP Decimal Hazard Detection	Error Capture Rate	97.0%	52.0%
Adversarial Tool Containment	Refusal Fidelity	94.0%	59.4%

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🚀 Quickstart & Inference

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_id = "google/gemma-2-2b-it"
adapter_id = "philgear/pocketgull-waveform-qrs-1d"

tokenizer = AutoTokenizer.from_pretrained(base_model_id)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
model = PeftModel.from_pretrained(base_model, adapter_id)

prompt = "Clinical query regarding 1D ECG Electrophysiology & Arrhythmia Detection."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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🔍 Independent Audit & Reproducibility

# Independent audit command via Inspect AI
pip install inspect-ai peft torch transformers
inspect eval test_clinical_safety.py --model hf/philgear/pocketgull-waveform-qrs-1d

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⚖️ Governance & Statutory Duty of Care Notice

This model adheres to the Digital Duty of Care & Safety by Design (SbD) framework:

• Statutory Compliance: Pre-flight evaluation against the Australian eSafety Commissioner Safety by Design principles.
• Clinical Harm Modeling: Risk profiled under the STRIDE-H (Human Harms) Threat Matrix.
• Non-Diagnostic Notice: This model is designed for clinical workflow validation, triage support, and safety auditing. It does not replace direct physician clinical judgment.

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📄 Citation & Attribution

@misc{pocketgull_waveform_qrs_1d_2026,
  author = {Phil Gear},
  title = {PocketGull Waveform QRS 1D (Biosignal Electrophysiology)},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/philgear/pocketgull-waveform-qrs-1d}}
}