Systems Sciences in Dentistry: A Critical Review
Bibliographic record
Abstract
Despite substantial clinical progress under the biomedical paradigm, global oral disease burdens remain high, highlighting the need for integrated and context-sensitive models. This critical review examines how systems sciences can contribute to redefining dentistry's role within contemporary health systems. While systems thinking dates back nearly a century, its application to dental research and policy has expanded significantly in recent years. The objectives of this review are to assess the relevance of systems sciences in addressing current health challenges involving dentistry and to analyze how these approaches have informed dental science and practice to date. Based on a thematic analysis of the literature, the review identifies emerging conceptual frameworks, practical applications across multiple dental domains, and areas requiring further exploration. The review distinguishes between predictive approaches, such as simulation and modeling, and relational paradigms rooted in interdependence, context, and goal-oriented dynamics. It documents recent applications of these frameworks in public health, clinical planning, education, environmental strategy, and patient care. Three widely shared assumptions are finally revisited through systems sciences perspectives: the framing of "oral health" as an isolated individual outcome, the dichotomy between "prevention" and "treatment," and the notion that oral diseases are largely "preventable." While such assumptions may serve communication or policy purposes, they can obscure structural determinants and limit systemic integration of actions against the burden of oral diseases. Systems sciences provide methodological and conceptual tools to reposition dentistry as a contributor to broader health goals: reducing population-level treatment needs, improving access, and addressing upstream determinants of health. Rather than promoting technical growth alone, dental research should place greater emphasis on systemic approaches to ensure that available resources effectively serve population needs. As a scientific framework, systems sciences are not only compatible with these aims but are also essential to achieving them in a coherent, evidence-based, and socially relevant manner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".