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.Although these recent events show animal health and human health to be integrally connected, few clear lines of communication exist between veterinary and medical professionals with respect to preventive medicine and public health, clinical practice or research.The potential advantages of greater integration 'across species' have been identified and documented by individuals involved in the 'One Health Initiative' (www.onehealthinitiative.com) (5).The One Health Initiative was founded by Dr Laura Kahn (a physician at Princeton University [New Jersey, USA]), Dr Bruce Kaplan (a retired veterinarian formerly with the United States Centers for Disease Control and Prevention, and the United States Department of Agriculture's Food Safety Inspection Service) and Dr Tom Monath (a physician and former Division Director at the Centers for Disease Control and Prevention, and the United States Army Medical Research Institute for Infectious Diseases).More recently, the group has been joined by Jack Woodall, a cofounder and Associate Editor of ProMED-mail.The One Health concept has won endorsement from many professional societies, including the American Medical Association (6) and the American Veterinary Medical Association (7), and has had its principles endorsed by the
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 | 0.024 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.020 | 0.057 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.042 | 0.079 |
| Insufficient payload (model declined to judge) | 0.026 | 0.011 |
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".