Bibliographic record
Abstract
von Leeuwenhoek discovered microbes. He was an early user of the microscope and analyzed small scrapings from teeth. He described small “animalcules,” which later were named microbes and we call bacteria. Two hundred years later in 1876, Robert Koch proposed the “germ theory of disease, ” suggesting that bacteria may cause disease.1 At the same time, Edward Jenner, Joseph Lister, and Louis Pasteur also implicated germs as a possible source of disease. In 1879, Willoughby D. Miller, a recent graduate of the University of Pennsylvania Dental School, heard of Koch’s theory that germs might cause disease and determined that he too wanted to study bacteria. On completing his dental training, he trav-eled to Berlin where he began work within Koch’s institute, looking at the relationship of bacteria to disease. Miller became convinced that the mouth was a focus of infection and that bacteria in the mouth could explain most of human-kind’s illnesses. Simultaneously, William Hunter, a physician in London, was investigating what caused illness in his patients. Why did his patients develop high fevers and why were they dying? He heard Miller speak on “Oral Infection as a Cause of Systemic Disease.”2 After hearing Miller, Hunter realized that he had the answer: his patients developed illness because of the sepsis (bacteria) Is oral infection and inflammation a risk for systemic conditions? It has taken more than 300 years to arrive at the point we are today. The articles in this special issue, which are based on presentations at the International Consensus Forum on Oral and Systemic Health in Montreal, review the current evidence on the contribution of oral bacteria to systemic health. As a first step, it is helpful to review the past to see how the question of the oral/systemic connection evolved over three centuries. This article presents a brief overview of that journey.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.112 | 0.060 |
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".