Bibliographic record
Abstract
Atypical pneumonia was first described in 1938, and over time, Mycoplasma, Legionella, and Chlamydophila were the agents commonly linked with community-associated atypical pneumonia. However, as technology has improved, so has our understanding of this clinical entity. It is now known that there are many agents linked with atypical pneumonia in the community, and many of these agents are also major causes of healthcare-associated pneumonia. This article discusses the history, epidemiology, and pathogenesis of infection; control of infection; clinical findings; diagnosis; and, where applicable, treatment of the agents of healthcare-associated atypical pneumonia. Bacterial agents include Legionella species, Mycoplasma pneumoniae, Chlamydophila species, and Coxiella burnetii. Although there are over 100 viruses that can cause respiratory tract infections, only a fraction of those have been defined in the context of healthcare-associated atypical pneumonia: adenovirus and human bocavirus (HBoV); rhinovirus; human coronaviruses (HCoV), including HCoV 229E, HCoV OC43, HCoV NL63, HCoV HKU1; members of the paramyxoviridae (parainfluenza viruses, human metapneumovirus, and respiratory syncytial virus); hantavirus; influenza; and severe acute respiratory syndrome (SARS) Co-V. Our knowledge about healthcare-associated atypical pneumonia will continue to evolve as newer pathogens are identified and as newer diagnostic modalities such as multiplex polymerase chain reaction are introduced.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".