Bacterial Infections of the Lung in Normal and Immunodeficient Patients
Bibliographic record
Abstract
The lung is exposed to enormous quantities of air and to potentially infectious agents, but serious infections rarely occur, a testament to the extraordinary natural defences of the respiratory tract. The most common causes of bacterial lung infections in normal hosts include Streptococcus pneumoniae, Haemophilus species, Staphylococcus aureus and Mycobacterium tuberculosis. In compromised hosts, the bacterial causes of pneumonia are much broader, including species not usually considered of high virulence in humans. Indeed infection with one of these unusual bacterial species demands a search for an immunocompromising condition. Normal defences of the respiratory tract include non-specific physical factors (the 'mucociliary escalator'), and innate factors, including defensins, lysozyme and phagocytic cells (polymorphonuclear leukocytes, pulmonary alveolar macrophages and dendritic cells). Antibacterial defences are enhanced by opsonins, including those intrinsically present (surfactant and complement components) and induced immunoglobulins. Immunocompromising conditions, in which bacterial lung infections frequently occur, include (but are not limited to) hypogammaglobulinaemia, chronic granulomatous disease and primary ciliary dyskinesia. Each of these conditions illustrates the essential role of the disabled element of the innate and adaptive immune system in maintaining sterility of the lower respiratory tract.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".