Nontuberculous Mycobacteria in Horses: A Narrative Review
Bibliographic record
Abstract
Nontuberculous mycobacteria (NTM) infections are increasing in human and veterinary medicine. Although horses were initially thought to be resistant to NTM infection, reports of horses suffering from gastrointestinal, respiratory, and reproductive diseases associated with NTM have increased in the last few decades. The aim of this literature review is to summarize the mycobacteria species found in horses, describe clinical manifestations, diagnostic and treatment approaches, and public health concerns of NTM infection in horses. Clinical manifestations of NTM in horses include pulmonary disease, lymphadenitis, soft tissue, bone infections, and disseminated disease. NTM are also linked to granulomatous enteritis, placentitis, and abortions. Currently, diagnostic methods for NTM are limited and include acid-fast microscopy, bacterial cultures, species-specific PCR assays, and gene sequencing. In humans, NTM treatment guidelines are available, but their application appears inadequate and inconsistent. In horses, treatment guidelines for NTM infections are not available. NTM are a serious public health threat as 70% of people with untreated acquired immunodeficiency syndrome (AIDS) have a chronic pulmonary disease caused by NTM. Thus, it is essential that we gain a better understanding of NTM infections in horses and their zoonotic potential.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".