ClinQSphereX
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Diabetes Research Intelligence

Diabetes protocols lean on a small set of repeatable laboratory values, so eligibility logic is unusually explicit — and a useful baseline for comparing screening approaches.

Scientific visualization of metabolic molecular structures
Illustrative scientific visualization — not patient imagery or a clinical finding.

Screening support only. ClinQSphereX provides research and recruitment workflow support. It does not independently determine clinical eligibility, diagnosis, treatment or enrolment. Candidates are shown as potentially eligible and require researcher review.

Conditions in scope

  • Type 1 diabetes
  • Type 2 diabetes
  • Diabetic kidney disease
  • Diabetic neuropathy

Research variables

Synthetic data
  • Age
  • HbA1c
  • Fasting glucose
  • BMI
  • Blood pressure
  • Duration of disease
  • Medication history
  • Recorded complications

Variables are only used when they exist in the underlying dataset. Demonstration records are synthetic and are not real patient data.

Why screening is hard here

Thresholds are explicit; the challenge is value recency, not interpretation.

Clinical trial discovery

Live data

Records are retrieved from the public ClinicalTrials.gov registry when you search. Nothing on this panel is a determination about any individual.

Searching the registry…

Structured eligibility screening

Synthetic data

Protocol text becomes one row per criterion. Each row records the expected value and the observed value, and resolves to PASS, FAIL or UNKNOWN. Missing information stays UNKNOWN — it is never converted into a pass.

Illustrative synthetic screening example for Diabetes
CriterionExpectedIllustrative outcome
Age18–75 yearsPASS
HbA1c7.0–10.5%FAIL
BMI25–45UNKNOWN
Duration of diagnosis≥ 6 monthsPASS
Current insulin therapyProtocol dependentFAIL

Illustrative synthetic research profile. Outcomes shown here demonstrate the display format only; live outcomes are computed inside the workspace from your own data.

The decision stays with a researcher

  1. 01

    Model prediction

    Ranked shortlist with a confidence level.

  2. 02

    Evidence

    Observed value, source and timestamp per criterion.

  3. 03

    Explanation

    Feature contributions behind the prediction.

  4. 04

    Researcher review

    Pending review · Requires more evidence · Not relevant · Potentially relevant.

  5. 05

    Research action

    Outreach, screening visit or exclusion — recorded with the reviewer.

Limitations for this area

Other disease areas