Radiological imaging in pneumonia: recent innovations
Bibliographic record
Abstract
PURPOSE OF REVIEW: Pneumonia is one of the major infectious diseases responsible for significant morbidity and mortality throughout the world. Radiological imaging plays a prominent role in the evaluation and treatment of patients with pneumonia. This paper reviews recent innovations in the radiologic diagnosis and management of suspected pulmonary infections. RECENT FINDINGS: Chest radiography is the most commonly used imaging tool in pneumonias because of availability and an excellent cost-benefit ratio. Computed tomography is mandatory in unresolved cases or when complications of pneumonia are suspected. A specific radiologic pattern can suggest a diagnosis in many cases. Bacterial pneumonias are classified into four main groups: community-acquired, aspiration, healthcare-associated and hospital-acquired pneumonia. The radiographic patterns of community-acquired pneumonia may be variable and are often related to the causative agent. Aspiration pneumonia involves the lower lobes with bilateral multicentric opacities. The radiographic patterns of healthcare-associated and hospital-acquired pneumonia are variable, most commonly showing diffuse multifocal involvement and pleural effusion. SUMMARY: Combination of pattern recognition with knowledge of the clinical setting is the best approach to the radiologic interpretation of pneumonia. Radiological imaging will narrow the differential diagnosis of direct additional diagnostic measures and serve as an ideal tool for follow-up examinations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".