ClinQSphereX
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Rare Diseases Research Intelligence

Rare disease recruitment is the hardest case: very few potentially eligible people, distributed across many sites and registries. Structured screening matters most where the population is smallest.

Scientific visualization of a DNA double helix
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

  • Genetic metabolic disorders
  • Neuromuscular disorders
  • Rare haematological disorders

Research variables

Synthetic data
  • Age
  • Confirmed diagnosis code
  • Genetic finding (where recorded)
  • Relevant laboratory values
  • Prior treatment history

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

Tiny candidate pools; a single UNKNOWN can remove the only potential match.

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 Rare Diseases
CriterionExpectedIllustrative outcome
Confirmed diagnosisPresentPASS
Genetic confirmationWhere required by protocolFAIL
AgeProtocol dependentUNKNOWN
Prior treatmentRecordedPASS

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