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GEHC

Trust-weighted public proof page for GEHC. See which authors support it, which ticker theses it belongs to, and how thesis calls have performed.

Opportunity
5 / 100
Current score
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Thesis calls
3
Active decisions
0

Recent proof-backed thesis calls

Public preview of asset-level thesis calls linked to source content, observed prices, and outcomes.

Scientific paper proposes a unified benchmark (60 healthy subjects, 3 cadences) to predict hip muscle forces and joint moments directly from gait kinematics using sequence models; Transformer performed best and showed only moderate zero-shot generalization to a small external pathological cohort (9 ONFH patients). Investable implication is not the specific model, but acceleration/automation of gait analytics and biomechanics-derived metrics from cheaper kinematics inputs (wearables/markerless ca

Mentioned: Jun 1, 2026, 12:00 AM EDTConviction: 24 / 100
Source: Gait2Hip-60: A Unified Deep Learning Benchmark for Predicting Hip Muscle Forces and Joint Moments from Multi-Cadence Gait Kinematics
yush_gxopen

A researcher claims they can induce distinct “ghost smells” (e.g., campfire, garbage truck) by targeting the brain with ultrasound—described as rapid to reproduce and (to their knowledge) novel even in animals. This is early-stage neurotech/sensory-interface research with unclear commercialization timeline and no direct public-company linkage yet.

Mentioned: Jun 17, 2026, 11:04 PM EDTConviction: 18 / 100
Source: Pinned Yush @yush_g Nov 21, 2025 We made fake smells that don't exist. Creating ghost smells with ultrasound only too...
Stanford Onlineyoutubeopen

Only a title/body were provided; no transcript, link, speaker names, or concrete technical claims to verify. From the topic (“AI in healthcare,” “open evidence,” “cyber risks”), the most plausible tradable implications are: (1) increased adoption of AI/LLMs in clinical workflow and imaging, (2) stronger demand for healthcare data infrastructure/interop tooling, and (3) heightened healthcare cybersecurity spend due to AI-enabled attack surface and regulatory scrutiny. All conclusions are high-unc

Mentioned: Jun 15, 2026, 7:06 PM EDTConviction: 36 / 100
Source: AI in Healthcare Series: Inside the Rise of AI in Healthcare, Open Evidence and Cyber Risks

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GEHC
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