The ‘One Health’ Paradigm: Time for Infectious Diseases Clinicians to Take Note?
Bibliographic record
Abstract
W hat do severe acute respiratory syndrome (SARS), monkeypox, highly pathogenic influenza A, new variant Creutzfeld-Jacob disease, cryptosporidiosis and verotoxigenic Escherichia coli have in common? All represent infectious diseases that have emerged, been recognized or changed their distribution markedly over the past three decades, and all may be considered to be 'zoonotic' threats; ie, they are diseases (or commensal microbes) found in animals that can be transmitted to humans, causing disease in the latter. The preponderance of zoonoses among emerging infectious diseases is striking: a pre-SARS Institute of Medicine (USA) report (1) suggested that approximately three-quarters of emerging infections originated in animals. Zoonotic threats become an even greater menace when combined with the rapidity of air travel, and the high volumes of animal trafficking and smuggling that currently occur. SARS was an infectious threat that moved from a natural reservoir (likely bats) to civet cats in animal markets in Guangdong province, China, and were then carried by infected humans to the hospitals of Toronto, Ontario, in a few short months (2). Similarly, an Old World virus causing West Nile virus fever and encephalitis emerged as a novel infectious entity in the western hemisphere in 1999, possibly following illicit animal importation into New York (USA) (3,4). The list goes on.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".