Biomedical science often accumulates data without theoretical convergence because key constructs remain semantically under-specified. Using the central venous pressure (CVP)-preload debate as a case study, this review shows how definitional drift creates “semantic pseudoreplication,” where studies reuse the same terms while testing non-equivalent hypotheses, preventing decisive falsification. We argue that knowledge-engineering tools (ontologies, knowledge graphs, computational semantics) could provide machine-interpretable hypothesis definitions that stabilize meaning and improve epistemic eficiency. Finally, we propose an Incremental Epistemic Efectiveness Ratio (IEER) to quantify knowledge gained per research investment once hypotheses are formally specified.
Biomedical science is data-rich but conceptually fragile. The long-standing debate over whether central venous pressure is a clinically relevant predictor of cardiac preload and fluid-responsiveness exemplifies how decades of data can accumulate without theoretical convergence when key terms lack fixed meaning. We argue that problem lies not in measurement but in the absence of formal semantics at the level of hypothesis definition.
Unlike physics, which anchors concepts in mathematical formalism, medicine relies on natural language and shifting usage. Constructs such as “preload” or “volume status” blur theoretical and operational boundaries, producing apparent progress without conceptual closure.
Knowledge engineering ofers a remedy. Through formal logic, ontologies and knowledge graphs, it enables machine-interpretable representations of biomedical entities and relations, though these tools rarely extend to hypothesis formulation. This review argues that semantic formalization can clarify biomedical reasoning itself. The CVP–preload case shows how definitional vagueness generates pseudo-cumulative research—and how, with semantic precision, an Incremental Epistemic Efectiveness Ratio (IEER) could quantify the knowledge gained per research investment.
This review investigates how semantic ambiguity in biomedical research constrains theoretical progress and how formal semantic frameworks could enhance conceptual precision, falsifiability, and epistemic ⋆This short paper represents an initial exploration of the topic and is currently being expanded into a more comprehensive publication as research continues to develop.
CEUR Workshop
ISSN1613-0073 eficiency. Using the controversy over central venous pressure (CVP) and preload as a case study, it examines how definitional drift sustains pseudo-cumulative inquiry and how semantic formalization could realign biomedical science with the clarity characteristic of the physical sciences. 2.2. Research Questions • How has semantic vagueness shaped the formulation, testing, and persistence of the CVP–preload hypothesis in biomedical research? • What can the comparison with fields like physics reveal about the role of formal semantics in enabling decisive falsification and theoretical convergence? • Which knowledge-engineering and formalization approaches (e.g., ontologies, computational semantics) could provide the infrastructure for precise, machine-interpretable biomedical hypotheses? • What are the socio-economic costs associated with persistent semantic ambiguity in biomedical research, exemplified by the CVP–preload debate?
A targeted literature search examined how central venous pressure (CVP) and preload have been
defined and investigated in biomedical research, focusing on conceptual evolution rather than exhaustive
coverage. The recent narrative review by Lloyd-Donald et al. [
Definitional drift clouds studies of fluid responsiveness, a concept itself variably defined. Central venous pressure (CVP) and cardiac preload have long been conflated: CVP measured variably as static pressure, dynamic response, or mechanically influenced value, while cardiac preload—physiologically the ventricular end-diastolic stretch that determines stroke volume via the Frank-Starling relationship—has often been interpreted loosely as right ventricular end-diastolic volume or wall stress. This shifting terminology created the illusion of coherent evidence. Meta-analyses concluding that CVP fails to predict fluid responsiveness reveal a deeper design flaw: studies were chasing vague relationships from the outset. Decades of research thus produced pseudo-replications—experiments linked by words but divided by meaning.
The CVP–preload debate exemplifies what Yarkoni [
The CVP–preload controversy echoes the 19th-century search for the luminiferous aether. Like CVP,
the aether was conceived as an invisible intermediary. The Michelson–Morley experiment initially
produced a null result, but its impact was decisive because the aether hypothesis had precise mechanical
definitions—its falsification was semantically unambiguous. When Dayton Miller later reported positive
fringe shifts in the 1920s, physicists could conclusively rebut his findings because the underlying
definitions and expectations were unchanged; the dispute could be resolved through numerical analysis
of the statistical results rather than conceptual reinterpretation. [
In medicine, no such semantic infrastructure exists. “CVP” and “preload” remain elastic, allowing each negative result to be reinterpreted rather than resolved. Physics advanced by redefining its ontology; medicine, lacking formal semantics, continues to accumulate exceptions instead of discarding untenable constructs. Physics could abandon the aether because its constructs—velocity, mass, field—were defined within a formal mathematical language that fixed meaning and enabled falsification. Medicine, however, still relies on natural language: key terms such as “preload,” “tone,” or “compliance” remain metaphorical and context-dependent.
Today, we possess tools that could close this gap. Ontologies like the OBO Foundry and SNOMED CT, systems biology models, and knowledge graphs can encode physiological entities and relations with logical precision. Yet these frameworks are rarely applied to hypothesis definition, leaving medicine rich in data but poor in semantic discipline. The CVP–preload debate shows that without formal semantics, negative findings cannot decisively falsify ambiguous hypotheses. Neurosymbolic AI ofers a promising avenue to bridge this divide: by combining symbolic reasoning over formal ontologies with statistical learning from large biomedical datasets, these hybrid systems can both encode explicit conceptual definitions and infer novel patterns. This approach could automate hypothesis testing, detect inconsistencies in conceptual frameworks, and guide experimental design, efectively translating vast data into semantically coherent and actionable biomedical knowledge.
The decades-long debate over whether central venous pressure (CVP) reliably reflects preload has not
only stalled conceptual progress but also incurred substantial societal costs. A meta-review by Eskesen
et al. [
The case of central venous pressure illustrates a broader epistemological weakness in biomedical science: the absence of formal semantics at the level of hypothesis definition. Where physics evolved mathematical frameworks that enforce internal coherence and permit decisive falsification, medicine remains reliant on intuitive, metaphorical constructs whose meanings shift with context.
The CVP–preload debate has persisted not because physiology is mysterious, but because language is. Researchers have been testing diferent phenomena under the same name, generating an illusion of cumulative evidence. The result is a form of semantic pseudoreplication—the repetition of experiments that cannot, even in principle, converge on a shared truth condition.
If biomedical science is to attain the semantic stability that made physics cumulative, it must treat hypothesis definition as a formal act. Ontological modeling, computational semantics, and explicit definitional contracts could supply the missing infrastructure. Only then will negative experiments become not just failures to predict, but steps toward theoretical clarity. Under such semantic rigor, metrics like an Incremental Epistemic Efectiveness Ratio (IEER) could quantify the epistemic value of research, guiding funding toward studies that maximize knowledge gain relative to cost and transforming the allocation of resources into a principled, evidence-driven process.
The authors have no external funding to declare for the current research.
During the preparation of this work, the author(s) used GPT5 in order to: perform a grammar and spelling check, and provide alternative formulations for selected sentences. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the publication’s content.