Clinical Intuition Ordinal Hierarchy
Turn clinical intuition into numbers.
Experts can’t reliably rank twenty things — but they can reliably say which of two is more severe, more urgent, more important. CLIOH aggregates thousands of those small either/or judgments into a validated, interval-scaled ranking.
Which is more urgent?
Bradley–Terry worth, so far
A ranking emerges from many small either/or judgments. Illustrative — not a clinical tool.
The same machinery behind modern AI — and validated in the clinic
Pairwise, not Likert
Forced binary choices, no ties. People are noisy on absolute scales but sharp on “which of these two?” — so that’s all CLIOH ever asks.
The Bradley–Terry model
The votes fit a Bradley–Terry likelihood — the same one behind the reward models that align modern AI from human preference — yielding an interval scale with bootstrapped 95% confidence intervals.
Validated clinical precedent
Pairwise–to–Bradley–Terry scaling already works in medicine: an expert-comparison severity scale for retinopathy of prematurity (i-ROP, PMID 35157950) and patient pairwise weighting in knee osteoarthritis (Pedersen, PMID 34006025).
CLIOH isn’t one study — it’s a family of study types.
The same elicitation answers very different questions — ranking drivers, grading cases, prioritising in real time, weighting outcomes, benchmarking an AI. The shape of your question selects the type. Start one of two ways:
The sixteen types
Open the explorerResearch / Discovery CLIOH
rank candidate drivers of a phenomenon to aim the next study.
Educational / Training CLIOH
make the tacit priority order of senior faculty explicit and teachable — convert "you'll just know it when you've seen enough" into a measured, curriculum-ready hierarchy.
Classification / Nosology CLIOH
turn a severity continuum into outcome-validated categorical grades.
Triage / Priority CLIOH
map case features → an urgency score for live prioritization (OR booking, ED throughput, transport).
Calibration / Assessment CLIOH
measure how well an individual's clinical judgment agrees with an expert-consensus reference scale — i.e., score a person, not a variable.
Outcome-Importance CLIOH
rank which outcomes a trial (or a condition's management) should measure — producing an interval-scale, patient-aligned endpoint hierarchy.
Risk-Factor Weighting CLIOH
turn expert pairwise judgments of predictor variables into a weighted clinical prediction-rule score — an expert prior you can deploy before big-N outcome data exists, then validate empirically later.
Patient-Centered Preference CLIOH
measure population-level patient/family preference weights over treatment attributes and burdens, to inform shared decision-making and labeling.
AI / Algorithm Calibration CLIOH
label or benchmark a clinical AI's outputs against an expert-consensus interval scale, so the model is measured on the surgeons' own ruler — not an arbitrary one.
Resource / Implementation Priority CLIOH
rank a portfolio of competing interventions, projects, or sites for finite funding or QI-deployment effort — turning a contentious budget meeting into a measured, defensible priority order.
Phenotype / Classification Boundary CLIOH
locate the decision boundary between two or more discrete clinical phenotypes — not rank a single severity dimension.
Diagnostic Hierarchy CLIOH
given a patient's presenting features, produce a feature-conditional ordering of the differential diagnosis — which condition is most likely, second, third — as a decision-support aid.
Calibration × Educational CLIOH
from one faculty elicitation, produce both a teaching tool (Educational §2) and a trainee assessment (Calibration §5).
Discovery × Outcome-Importance CLIOH
combine variable discovery (Discovery §1) with endpoint ranking (Outcome-Importance §6) to produce a complete multicenter-trial-design package — which variables to measure and which outcomes to power on.
Classification × AI-Calibration CLIOH
turn an expert classification worth scale (Classification §3) into the continuous regression target that trains or benchmarks a clinical AI (AI-Calibration §9).
Triage × Risk-Factor CLIOH
from one covariate-structured elicitation, get both a live prioritization score (Triage §4) and the implicit risk-factor weights (Risk-Factor §7) as a second deliverable.
CLIOH is a methodology for measuring expert clinical intuition. It is not a clinical decision-support tool. Triage-style outputs are not intended for patient-level decisions until separately reviewed, prospectively validated, and instrumented to the appropriate quality standard.