RESEARCH INFRASTRUCTURE
Research Infrastructure, Not Research Burden
SpineSense turns every clinical interaction into research-grade data. Natural language queries, automated cohort assembly, and IRB-compatible collaboration tools, all built into the workflow you're already using.
THE DATA PROBLEM
Millions of Surgeries. Almost No Usable Data.
Over the last decade, surgeons have performed more than 16 million spine procedures in the United States — a $100 billion annual industry. Yet the prospective clinical trials guiding surgical decisions average just 130–150 patients. This isn't a compute problem or an algorithm problem. It's a data problem.
The Volume–Precision Paradox
Spine Surgeries Performed (Last Decade)
That's 0.0009% of the real-world patient population captured in our best research.
Avg. Prospective Clinical Trial Sample Size
A Decade of Surgical Volume Growth
Between 2014 and 2024, US spine surgery volume grew consistently — over 13 million procedures performed. Each one generated imaging, operative reports, outcomes data. In an ideal system, every data point would power our understanding of rare complications.
Estimated US volume (instrumented fusion + decompression) based on market analysis trends
What Our Best Research Looks Like
Average sample sizes across spine surgical research domains
Large volume, but structurally shallow — relies on billing codes (ICD-10, CPT) that lack clinical granularity like alignment measurements or PROMs.
Studies averaging ~160 patients, yet 48.8% of analyzed outcomes had follow-up losses exceeding the fragility index.
Average of 153 patients with a growth rate of only ~4.5 patients/year over three decades.
Among RCTs reporting negative results, 0% had adequate statistical power to detect small effect sizes.
A bibliometric analysis of 20 recent RCTs found a total of just 954 participants across all studies.
Sample sizes so small that rare but devastating complications are statistically invisible in individual studies.
81% of all SCI studies meet the definition of ‘small-sample size study’ — fewer than 20 subjects per group.
Level IV evidence (retrospective case series) comprises 60–78% of published spine research. Level I evidence (high-quality RCTs) accounts for just 4–4.7%.
The Research Reality
Analysis of ~5,000 retrospective spine papers over the last decade reveals a stark pattern: 78% are retrospective case series. The distribution of sample sizes is heavily right-skewed — small studies dominate.
Key Statistics
The Fragility Problem
4–5
patient outcome reversals
The median number of patients whose outcomes would need to change to completely invalidate the conclusions of landmark spine surgery trials.
48.8%
of study conclusions potentially invalidated by missing data alone
In endoscopic decompression research, nearly half of all analyzed outcomes had more patients lost to follow-up than the number needed to reverse the study’s conclusion. The patients who simply didn’t come back could have flipped the result, and no one would know.
0%
of negative-result trials adequately powered
Among orthopedic RCTs reporting negative results, zero percent had adequate statistical power to detect a small effect size. The average sample size used was just 10% of what was mathematically required.
Postoperative Visual Loss (POVL)
0.006% incidence
Irreversible blindness following spine surgery. At this incidence rate, a trial of 153 patients has a near-zero probability of encountering even a single case. Identifying the intraoperative risk factors (MAP fluctuations, fluid resuscitation, hematocrit thresholds) is mathematically impossible in standard prospective trials.
Identified only through retrospective analysis of 4.7 million cases in the NIS database
Adjacent Segment Disease (ASDis)
26% 10-year prevalence
One in four fusion patients develops accelerated degeneration at adjacent levels within a decade. Detecting the predictive signals requires tracking radiographic parameters alongside functional scores over 5–10 years. In studies under 150 patients, these biomechanical patterns are drowned out by statistical noise.
PROM follow-up capture in registries hovers at just 31–42%, further reducing effective sample size
The Quality vs. Quantity Trade-Off
Why not just use big national databases (NIS/NSQIP)? Because we lose granularity. Large datasets lack specific CPT modifiers, precise Cobb angles, or patient-reported outcomes. Small studies have the detail but lack the power. The ideal “Big Data” zone is currently empty.
The data exists. The infrastructure to capture it does not.
What researchers are working with today
- Avg. 130–150 patients per prospective trial
- Fragility Index of 4–5 (study conclusions hinge on a handful of patients)
- EHR data fragmented across proprietary vendor silos
- Free-text operative notes requiring expensive manual extraction
- PROM follow-up capture rates of only 31–42%
- Level I evidence comprises just 4.7% of published spine research
What SpineSense makes possible
- Structured data captured at point of care, with no manual abstraction
- Spine-specific ontology with standardized, interoperable labeling
- Automated longitudinal PROM tracking integrated into clinical workflow
- Imaging measurements with provenance metadata and version control
- Data quality monitoring with drift detection and completeness scoring
- Scalable infrastructure designed for multi-institutional aggregation
Capturing even 20% of U.S. spine surgeons (~1,000 surgeons) with prospectively structured, outcomes-linked cases would produce a dataset larger than the entire American Spine Registry's first three years — and orders of magnitude more clinically granular than any administrative claims database.
Statistical Power Simulator
Why Small Studies Miss Subtle Patterns
Subtle patterns, like a 1% increase in infection rate for a specific diabetic profile, are statistically invisible to small studies. Adjust the parameters below to see how many patients you actually need versus what typical studies provide.
Drag to model different complication rates
Required vs. Available
CRITICAL GAP: You need 22× more patients than the median study provides. Subtle patterns at this event rate are completely invisible.
THE POWER OF SCALE
Subtle Patterns Emerge Only at Scale
Rare complications and multifactorial risk patterns are statistically invisible in small studies. Watch how increasing sample size reveals what was always there — hidden in the noise.
Sample Size
ML Pattern Detection
Inactive
68
Patients
3
Events
4.4%
Event Rate
Patient Outcome Distribution
This simulation uses synthetic data modeling real spine surgery complication distributions. In practice, SpineSense captures 200+ structured variables per case: imaging measurements, implant metadata, PROMs, and intraoperative parameters, enabling ML models to detect interaction effects across dozens of dimensions simultaneously.